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Journal articles on the topic 'GeoAI'

Author: Grafiati
Published: 4 June 2021
Last updated: 21 February 2023

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1

Gonzales-Inca, Carlos, Mikel Calle, Danny Croghan, Ali Torabi Haghighi, Hannu Marttila, Jari Silander, and Petteri Alho. "Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends." Water 14, no. 14 (July 13, 2022): 2211. http://dx.doi.org/10.3390/w14142211.

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This paper reviews the current GeoAI and machine learning applications in hydrological and hydraulic modeling, hydrological optimization problems, water quality modeling, and fluvial geomorphic and morphodynamic mapping. GeoAI effectively harnesses the vast amount of spatial and non-spatial data collected with the new automatic technologies. The fast development of GeoAI provides multiple methods and techniques, although it also makes comparisons between different methods challenging. Overall, selecting a particular GeoAI method depends on the application’s objective, data availability, and user expertise. GeoAI has shown advantages in non-linear modeling, computational efficiency, integration of multiple data sources, high accurate prediction capability, and the unraveling of new hydrological patterns and processes. A major drawback in most GeoAI models is the adequate model setting and low physical interpretability, explainability, and model generalization. The most recent research on hydrological GeoAI has focused on integrating the physical-based models’ principles with the GeoAI methods and on the progress towards autonomous prediction and forecasting systems.
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Hu, Yingjie, Song Gao, Dalton Lunga, Wenwen Li, Shawn Newsam, and Budhendra Bhaduri. "GeoAI at ACM SIGSPATIAL." SIGSPATIAL Special 11, no. 2 (December 17, 2019): 5–15. http://dx.doi.org/10.1145/3377000.3377002.

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Lunga, Dalton, Yingjie Hu, Shawn Newsam, Song Gao, Bruno Martins, Lexie Yang, and Xueqing Deng. "GeoAI at ACM SIGSPATIAL." SIGSPATIAL Special 13, no. 1-3 (November 2021): 21–32. http://dx.doi.org/10.1145/3578484.3578491.

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Geospatial Artificial Intelligence (GeoAI) is an interdisciplinary field enjoying tremendous adoption. However, the efficient design and implementation of GeoAI systems face many open challenges. This is mainly due to the lack of non-standardized approaches to artificial intelligence tool development, inadequate platforms, and a lack of multidisciplinary engagements, which all motivate domain experts to seek a shared stage with scientists and engineers to solve problems of significant impact on society. Since its inception in 2017, the GeoAI series of workshops has been co-located with the Association for Computing Machinery International Conference on Advances in Geographic Information Systems. The workshop series has fostered a nexus for geoscientists, computer scientists, engineers, entrepreneurs, and decision-makers, from academia, industry, and government to engage in artificial intelligence, spatio-temporal data computing, and geospatial data science research, motivated by various challenges. In this article, we revisit and discuss the state of GeoAI open research directions, the recent developments, and an emerging agenda calling for a continued cross-disciplinary community engagement.
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Hu, Yingjie, Song Gao, Shawn Newsam, and Dalton Lunga. "GeoAI 2018 workshop report the 2nd ACM SIGSPATIAL international workshop on GeoAI." SIGSPATIAL Special 10, no. 3 (January 15, 2019): 16. http://dx.doi.org/10.1145/3307599.3307609.

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Li, Wenwen, and Chia-Yu Hsu. "GeoAI for Large-Scale Image Analysis and Machine Vision: Recent Progress of Artificial Intelligence in Geography." ISPRS International Journal of Geo-Information 11, no. 7 (July 11, 2022): 385. http://dx.doi.org/10.3390/ijgi11070385.

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GeoAI, or geospatial artificial intelligence, has become a trending topic and the frontier for spatial analytics in Geography. Although much progress has been made in exploring the integration of AI and Geography, there is yet no clear definition of GeoAI, its scope of research, or a broad discussion of how it enables new ways of problem solving across social and environmental sciences. This paper provides a comprehensive overview of GeoAI research used in large-scale image analysis, and its methodological foundation, most recent progress in geospatial applications, and comparative advantages over traditional methods. We organize this review of GeoAI research according to different kinds of image or structured data, including satellite and drone images, street views, and geo-scientific data, as well as their applications in a variety of image analysis and machine vision tasks. While different applications tend to use diverse types of data and models, we summarized six major strengths of GeoAI research, including (1) enablement of large-scale analytics; (2) automation; (3) high accuracy; (4) sensitivity in detecting subtle changes; (5) tolerance of noise in data; and (6) rapid technological advancement. As GeoAI remains a rapidly evolving field, we also describe current knowledge gaps and discuss future research directions.
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Li, H., and A. Zipf. "A CONCEPTUAL MODEL FOR CONVERTING OPENSTREETMAP CONTRIBUTION TO GEOSPATIAL MACHINE LEARNING TRAINING DATA." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2022 (June 1, 2022): 253–59. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2022-253-2022.

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Abstract. In the recent decade, Volunteered Geographical Information (VGI), in particular the OpenStreetMap (OSM), has helped to fill substantial data gaps in base maps, especially in Global South, thus has become a promising source of massive, free training data together with rich and detailed semantic information for geospatial artificial intelligence (GeoAI) applications. Although intensive works have explored the potential of generating training data from OSM, a systematic approach of harvesting OSM contribution as quality-aware training data for different GeoAI tasks is still missing. To fill this research gap, we proposed a conceptual model consisting of three major components: historical OSM and external datasets, quality indicators, and GeoAI models. As a proof of concept, we validated our conceptual model with an example task of detecting OSM missing buildings in Mozambique, where the impact of different error sources (e.g., completeness, alignment, rotation) in training data were compared and investigated in a quantitative manner. The lessons learned in this paper shed important lights on cooperating OSM data quality aspects with the development of more explainable GeoAI models.
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Gao, Song, Shawn Newsam, Liang Zhao, Dalton Lunga, Yingjie Hu, Bruno Martins, Xun Zhou, and Feng Chen. "GeoAI 2019 workshop report: The 3nd ACM SIGSPATIAL International Workshop on GeoAI: AI for Geographic Knowledge Discovery." SIGSPATIAL Special 11, no. 3 (February 13, 2020): 23–24. http://dx.doi.org/10.1145/3383653.3383662.

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8

Hsu, Chia-Yu, Wenwen Li, and Sizhe Wang. "Knowledge-Driven GeoAI: Integrating Spatial Knowledge into Multi-Scale Deep Learning for Mars Crater Detection." Remote Sensing 13, no. 11 (May 28, 2021): 2116. http://dx.doi.org/10.3390/rs13112116.

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This paper introduces a new GeoAI solution to support automated mapping of global craters on the Mars surface. Traditional crater detection algorithms suffer from the limitation of working only in a semiautomated or multi-stage manner, and most were developed to handle a specific dataset in a small subarea of Mars’ surface, hindering their transferability for global crater detection. As an alternative, we propose a GeoAI solution based on deep learning to tackle this problem effectively. Three innovative features are integrated into our object detection pipeline: (1) a feature pyramid network is leveraged to generate feature maps with rich semantics across multiple object scales; (2) prior geospatial knowledge based on the Hough transform is integrated to enable more accurate localization of potential craters; and (3) a scale-aware classifier is adopted to increase the prediction accuracy of both large and small crater instances. The results show that the proposed strategies bring a significant increase in crater detection performance than the popular Faster R-CNN model. The integration of geospatial domain knowledge into the data-driven analytics moves GeoAI research up to the next level to enable knowledge-driven GeoAI. This research can be applied to a wide variety of object detection and image analysis tasks.
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Bordogna, Gloria, and Cristiano Fugazza. "Artificial Intelligence for Multisource Geospatial Information." ISPRS International Journal of Geo-Information 12, no. 1 (December 30, 2022): 10. http://dx.doi.org/10.3390/ijgi12010010.

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Mao, Huina, Yingjie Hu, Bandana Kar, Song Gao, and Grant McKenzie. "GeoAI 2017 workshop report: the 1st ACM SIGSPATIAL International Workshop on GeoAI: @AI and Deep Learning for Geographic Knowledge Discovery." SIGSPATIAL Special 9, no. 3 (January 9, 2018): 25. http://dx.doi.org/10.1145/3178392.3178408.

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Papadakis, Emmanuel, Ben Adams, Song Gao, Bruno Martins, George Baryannis, and Alina Ristea. "Explainable artificial intelligence in the spatial domain ( X‐GeoAI )." Transactions in GIS 26, no. 6 (September 2022): 2413–14. http://dx.doi.org/10.1111/tgis.12996.

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12

Usery, E. Lynn. "GeoAI for Topographic Mapping and the Intelligent National Map." Abstracts of the ICA 3 (December 13, 2021): 1. http://dx.doi.org/10.5194/ica-abs-3-298-2021.

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Usery, E. Lynn. "GeoAI for Topographic Mapping Feature Extraction to Knowledge Graph." Abstracts of the ICA 2 (October 9, 2020): 1. http://dx.doi.org/10.5194/ica-abs-2-39-2020.

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Abstract. The U.S Geological Survey is exploring the use of machine learning and geospatial artificial intelligence (GeoAI) for topographic mapping tasks. These automated tasks include extracting topographic features such as hydrography, transportation, vegetation canopy, urban 3D structures, and others from raw data including lidar point clouds, color and near infrared images, historic topographic maps, and Web sources of existing geospatial resources. Current (2020) work includes extracting hydrography from elevation data, and geomorphic features with geographic names from historical topographical maps using Deep Learning. Extracted features are included in a geographic information system (GIS), supporting topographic mapping and modeling activities, and as semantic entities in a graph data model, building a knowledge graph for topographic data. These GIS datasets and topographic knowledge graphs can be used in automated topographic mapping processes and artificial intelligence routines that develop data for hydrologic, biologic, and geologic models that form part of the USGS EarthMap vision.
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Liu, Pengyuan, and Filip Biljecki. "A review of spatially-explicit GeoAI applications in Urban Geography." International Journal of Applied Earth Observation and Geoinformation 112 (August 2022): 102936. http://dx.doi.org/10.1016/j.jag.2022.102936.

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15

Kausika, Bala Bhavya, Diede Nijmeijer, Iris Reimerink, Peter Brouwer, and Vera Liem. "GeoAI for detection of solar photovoltaic installations in the Netherlands." Energy and AI 6 (December 2021): 100111. http://dx.doi.org/10.1016/j.egyai.2021.100111.

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Mai, Gengchen, Krzysztof Janowicz, Yingjie Hu, Song Gao, Bo Yan, Rui Zhu, Ling Cai, and Ni Lao. "A review of location encoding for GeoAI: methods and applications." International Journal of Geographical Information Science 36, no. 4 (January 24, 2022): 639–73. http://dx.doi.org/10.1080/13658816.2021.2004602.

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17

Züfle, Andreas. "Visions and challenges in GeoAI, ethics, and spatial quantum computing." SIGSPATIAL Special 11, no. 2 (December 17, 2019): 2–4. http://dx.doi.org/10.1145/3377000.3377001.

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18

Ludwig, Frank, Anja Medger, Hilmar B�rnick, Michael Opitz, Kathrin Lang, Michael G�ttfert, and Isolde R�ske. "Identification and Expression Analyses of Putative Sesquiterpene Synthase Genes in Phormidium sp. and Prevalence of geoA-Like Genes in a Drinking Water Reservoir." Applied and Environmental Microbiology 73, no. 21 (September 7, 2007): 6988–93. http://dx.doi.org/10.1128/aem.01197-07.

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ABSTRACT The occurrence of taste and odor problems in drinking water supplies is a widespread phenomenon. From a Saxonian water reservoir we isolated a cyanobacterial species which was classified as Phormidium sp. Under laboratory conditions it produced an earthy-musty smell due to the synthesis of geosmin. The only genes shown to be involved in geosmin biosynthesis are cyc2 and geoA of Streptomyces. Based on the alignment of Cyc2 with a putative sesquiterpene synthase of Nostoc punctiforme, a degenerate primer pair was designed. By PCR, we could amplify two similar genes in Phormidium sp., which we named geoA1 and geoA2. Their expression was studied by reverse transcription-PCR. This revealed that both genes are expressed at 20�C and a light-dark cycle of 12 h. Expression was not detectable at the end of a 24-h dark period. To analyze the prevalence of geoA1 and geoA2 in samples from the phytobenthos, we generated PCR fragments with the same degenerate primer pair. Fifty-five different sequences that might represent geoA variants were obtained. The GC content ranged from 42% to 67%, suggesting that taxonomically very different bacteria might contain such genes.
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19

Ballesteros, John R., German Sanchez-Torres, and John W. Branch-Bedoya. "A GIS Pipeline to Produce GeoAI Datasets from Drone Overhead Imagery." ISPRS International Journal of Geo-Information 11, no. 10 (September 30, 2022): 508. http://dx.doi.org/10.3390/ijgi11100508.

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Drone imagery is becoming the main source of overhead information to support decisions in many different fields, especially with deep learning integration. Datasets to train object detection and semantic segmentation models to solve geospatial data analysis are called GeoAI datasets. They are composed of images and corresponding labels represented by full-size masks typically obtained by manual digitizing. GIS software is made of a set of tools that can be used to automate tasks using geo-referenced raster and vector layers. This work describes a workflow using GIS tools to produce GeoAI datasets. In particular, it mentions the steps to obtain ground truth data from OSM and use methods for geometric and spectral augmentation and the data fusion of drone imagery. A method semi-automatically produces masks for point and line objects, calculating an optimum buffer distance. Tessellation into chips, pairing and imbalance checking is performed over the image–mask pairs. Dataset splitting into train–validation–test data is done randomly. All of the code for the different methods are provided in the paper, as well as point and road datasets produced as examples of point and line geometries, and the original drone orthomosaic images produced during the research. Semantic segmentation results performed over the point and line datasets using a classical U-Net show that the semi-automatically produced masks, called primitive masks, obtained a higher mIoU compared to other equal-size masks, and almost the same mIoU metric compared to full-size manual masks.
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20

Kim, Geunhan. "Development of GeoAI-based Environmental Policy Establishment Support System : Analysis of The Relationship Between Land Cover and LST in Seoul." Journal of Climate Change Research 13, no. 6 (December 31, 2022): 859–67. http://dx.doi.org/10.15531/ksccr.2022.13.6.859.

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21

Ryu, Dongwoo. "New Opportunities and Challenges of Geo-ICT Convergence Technology: GeoCPS and GeoAI." Journal of the Korean Society of Mineral and Energy Resources Engineers 56, no. 4 (August 1, 2019): 387–97. http://dx.doi.org/10.32390/ksmer.2019.56.4.383.

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22

Janowicz, Krzysztof, Song Gao, Grant McKenzie, Yingjie Hu, and Budhendra Bhaduri. "GeoAI: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond." International Journal of Geographical Information Science 34, no. 4 (October 30, 2019): 625–36. http://dx.doi.org/10.1080/13658816.2019.1684500.

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23

Pierdicca, Roberto, and Marina Paolanti. "GeoAI: a review of artificial intelligence approaches for the interpretation of complex geomatics data." Geoscientific Instrumentation, Methods and Data Systems 11, no. 1 (June 2, 2022): 195–218. http://dx.doi.org/10.5194/gi-11-195-2022.

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Abstract. Researchers have explored the benefits and applications of modern artificial intelligence (AI) algorithms in different scenarios. For the processing of geomatics data, AI offers overwhelming opportunities. Fundamental questions include how AI can be specifically applied to or must be specifically created for geomatics data. This change is also having a significant impact on geospatial data. The integration of AI approaches in geomatics has developed into the concept of geospatial artificial intelligence (GeoAI), which is a new paradigm for geographic knowledge discovery and beyond. However, little systematic work currently exists on how researchers have applied AI for geospatial domains. Hence, this contribution outlines AI-based techniques for analysing and interpreting complex geomatics data. Our analysis has covered several gaps, for instance defining relationships between AI-based approaches and geomatics data. First, technologies and tools used for data acquisition are outlined, with a particular focus on red–green–blue (RGB) images, thermal images, 3D point clouds, trajectories, and hyperspectral–multispectral images. Then, how AI approaches have been exploited for the interpretation of geomatic data is explained. Finally, a broad set of examples of applications is given, together with the specific method applied. Limitations point towards unexplored areas for future investigations, serving as useful guidelines for future research directions.
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Udawalpola, M., A. Hasan, A. K. Liljedahl, A. Soliman, and C. Witharana. "OPERATIONAL-SCALE GEOAI FOR PAN-ARCTIC PERMAFROST FEATURE DETECTION FROM HIGH-RESOLUTION SATELLITE IMAGERY." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-M-3-2021 (August 10, 2021): 175–80. http://dx.doi.org/10.5194/isprs-archives-xliv-m-3-2021-175-2021.

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Abstract. Regional extent and spatiotemporal dynamics of Arctic permafrost disturbances remain poorly quantified. High spatial resolution commercial satellite imagery enables transformational opportunities to observe, map, and document the micro-topographic transitions occurring in Arctic polygonal tundra at multiple spatial and temporal frequencies. The entire Arctic has been imaged at 0.5 m or finer resolution by commercial satellite sensors. The imagery is still largely underutilized, and value-added Arctic science products are rare. Knowledge discovery through artificial intelligence (AI), big imagery, high performance computing (HPC) resources is just starting to be realized in Arctic science. Large-scale deployment of petabyte-scale imagery resources requires sophisticated computational approaches to automated image interpretation coupled with efficient use of HPC resources. In addition to semantic complexities, multitude factors that are inherent to sub-meter resolution satellite imagery, such as file size, dimensions, spectral channels, overlaps, spatial references, and imaging conditions challenge the direct translation of AI-based approaches from computer vision applications. Memory limitations of Graphical Processing Units necessitates the partitioning of an input satellite imagery into manageable sub-arrays, followed by parallel predictions and post-processing to reconstruct the results corresponding to input image dimensions and spatial reference. We have developed a novel high performance image analysis framework –Mapping application for Arctic Permafrost Land Environment (MAPLE) that enables the integration of operational-scale GeoAI capabilities into Arctic science applications. We have designed the MAPLE workflow to become interoperable across HPC architectures while utilizing the optimal use of computing resources.
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Udawalpola, Mahendra R., Amit Hasan, Anna Liljedahl, Aiman Soliman, Jeffrey Terstriep, and Chandi Witharana. "An Optimal GeoAI Workflow for Pan-Arctic Permafrost Feature Detection from High-Resolution Satellite Imagery." Photogrammetric Engineering & Remote Sensing 88, no. 3 (March 1, 2022): 181–88. http://dx.doi.org/10.14358/pers.21-00059r2.

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High-spatial-resolution satellite imagery enables transformational opportunities to observe, map, and document the micro-topographic transitions occurring in Arctic polygonal tundra at multiple spatial and temporal frequencies. Knowledge discovery through artificial intelligence, big imagery, and high-performance computing (HPC) resources is just starting to be realized in Arctic permafrost science. We have developed a novel high-performance image-analysis framework—Mapping Application for Arctic Permafrost Land Environment (MAPLE)—that enables the integration of operational-scale GeoAI capabilities into Arctic permafrost modeling. Interoperability across heterogeneous HPC systems and optimal usage of computational resources are key design goals of MAPLE. We systematically compared the performances of four different MAPLE workflow designs on two HPC systems. Our experimental results on resource utilization, total time to completion, and overhead of the candidate designs suggest that the design of an optimal workflow largely depends on the HPC system architecture and underlying service-unit accounting model.
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Wang, Sizhe, and Wenwen Li. "GeoAI in terrain analysis: Enabling multi-source deep learning and data fusion for natural feature detection." Computers, Environment and Urban Systems 90 (November 2021): 101715. http://dx.doi.org/10.1016/j.compenvurbsys.2021.101715.

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Demertzis, Konstantinos, and Lazaros Iliadis. "GeoAI: A Model-Agnostic Meta-Ensemble Zero-Shot Learning Method for Hyperspectral Image Analysis and Classification." Algorithms 13, no. 3 (March 7, 2020): 61. http://dx.doi.org/10.3390/a13030061.

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Deep learning architectures are the most effective methods for analyzing and classifying Ultra-Spectral Images (USI). However, effective training of a Deep Learning (DL) gradient classifier aiming to achieve high classification accuracy, is extremely costly and time-consuming. It requires huge datasets with hundreds or thousands of labeled specimens from expert scientists. This research exploits the MAML++ algorithm in order to introduce the Model-Agnostic Meta-Ensemble Zero-shot Learning (MAME-ZsL) approach. The MAME-ZsL overcomes the above difficulties, and it can be used as a powerful model to perform Hyperspectral Image Analysis (HIA). It is a novel optimization-based Meta-Ensemble Learning architecture, following a Zero-shot Learning (ZsL) prototype. To the best of our knowledge it is introduced to the literature for the first time. It facilitates learning of specialized techniques for the extraction of user-mediated representations, in complex Deep Learning architectures. Moreover, it leverages the use of first and second-order derivatives as pre-training methods. It enhances learning of features which do not cause issues of exploding or diminishing gradients; thus, it avoids potential overfitting. Moreover, it significantly reduces computational cost and training time, and it offers an improved training stability, high generalization performance and remarkable classification accuracy.
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Pinto-Hidalgo, Jairo J., and Jorge A. Silva-Centeno. "AmazonCRIME: un conjunto de datos y punto de referencia de Inteligencia Artificial Geoespacial para la clasificación de áreas potenciales vinculadas a Crímenes Ambientales Transnacionales en la Selva Amazónica." Revista de Teledetección, no. 59 (January 31, 2022): 1–21. http://dx.doi.org/10.4995/raet.2022.15710.

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In this article the challenge of detecting areas linked to transnational environmental crimes in the Amazon rainforest is addressed using Geospatial Intelligence data, open access Sentinel-2 imagery provided by the Copernicus programme, as well as the cloud processing capabilities of the Google Earth Engine platform. For this, a dataset consisting of 6 classes with a total of 30,000 labelled and geo-referenced 13-band multispectral images was generated, which is used to feed advanced Geospatial Artificial Intelligence models (deep convolutional neural networks) specialised in image classification tasks. With the dataset presented in this paper it is possible to obtain a classification overall accuracy of 96.56%. It is also demonstrated how the results obtained can be used in real applications to support decision making aimed at preventing Transnational Environmental Crimes in the Amazon rainforest. The AmazonCRIME Dataset is made publicly available in the repository: https://github.com/jp-geoAI/AmazonCRIME.git.
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Alastal, Abdelkhalek I., and Ashraf Hassan Shaqfa. "GeoAI Technologies and Their Application Areas in Urban Planning and Development: Concepts, Opportunities and Challenges in Smart City (Kuwait, Study Case)." Journal of Data Analysis and Information Processing 10, no. 02 (2022): 110–26. http://dx.doi.org/10.4236/jdaip.2022.102007.

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Huang, Yi, May Yuan, Yehua Sheng, Xiangqiang Min, and Yuwei Cao. "Using Geographic Ontologies and Geo-Characterization to Represent Geographic Scenarios." ISPRS International Journal of Geo-Information 8, no. 12 (December 10, 2019): 566. http://dx.doi.org/10.3390/ijgi8120566.

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Traditional Geographic Information Systems (GIS) represent the environment under reductionist thinking, which disaggregates a geographic environment into independent geographic themes. The reductionist approach makes the spatiotemporal characteristics of geo-features explicit, but neglects the holistic nature of the environment, such as the hierarchical structure and interactions among environmental elements. To fill this gap, we integrate the concept geographic scenario with the fundamental principles of General System Theory to realize the environmental complexity in GIS. With the integration, a geographic scenario constitutes a hierarchy of spatiotemporal frameworks for organizing environmental elements and subserving the exploration of their relationships. Furthermore, we propose geo-characterization with ontological commitments to both static and dynamic properties of a geographic scenario and prescribe spatial, temporal, semantic, interactive, and causal relationships among environmental elements. We have tested the utility of the proposed representation in OWL and the associated reasoning process in Semantic Web Rule Language (SWRL) rules in a case study in Nanjing, China. The case study represents Nanjing and the Nanjing presidential palace to demonstrate the connections among environmental elements in different scenarios and the support for information queries, evolution process simulation, and semantic inferences. The proposed representation encodes geographic knowledge of the environment, makes the interactions among environmental elements explicit, supports geographic process simulation, opens opportunities for deep knowledge mining, and grounds a foundation for GeoAI to discover geographic complexity and dynamics beyond the support of conventional theme-centric inquiries in GIS.
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Zhao, Xiangyu. "Adaptive and automated deep recommender systems." ACM SIGWEB Newsletter, Spring (April 2022): 1–4. http://dx.doi.org/10.1145/3533274.3533277.

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Dr. Xiangyu Zhao is an assistant professor of the school of data science at City University of Hong Kong (CityU). Prior to CityU, he completed his PhD (2021) at MSU under the advisory of Dr. Jiliang Tang, MS (2017) at USTC and BEng (2014) at UESTC. His current research interests include data mining and machine learning, especially (1) Personalization, Recommender System, Online Advertising, Search Engine, and Information Retrieval; (2) Urban Computing, Smart City, and GeoAI; (3) Deep Reinforcement Learning, AutoML, and Multimodal ML; and (4) AI for Social Computing, Finance, Education, Ecosystem, and Healthcare. He has published more than 30 papers in top conferences (e.g., KDD, WWW, AAAI, SIGIR, ICDE, CIKM, ICDM, WSDM, RecSys, ICLR) and journals (e.g., TOIS, SIGKDD, SIGWeb, EPL, APS). His research received ICDM'21 Best-ranked Papers, Global Top 100 Chinese New Stars in AI, CCF-Tencent Open Fund, Criteo Research Award, Bytedance Research Award and MSU Dissertation Fellowship. He serves as top data science conference (senior) program committee members and session chairs (e.g., KDD, AAAI, IJCAI, ICML, ICLR, CIKM), and journal reviewers (e.g., TKDE, TKDD, TOIS, CSUR). He serves as the organizers of DRL4KDD@KDD'19, DRL4IR@SIGIR'20, 2nd DRL4KD@WWW'21, 2nd DRL4IR@SIGIR'21, and a lead tutor at WWW'21/22 and IJCAI'21. He also serves as the founding academic committee members of MLNLP, the largest AI community in China with 800,000 members/followers. The models and algorithms from his research have been launched in the online system of many companies.
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Can, R., S. Kocaman, and A. O. Ok. "A WEBGIS FRAMEWORK FOR SEMI-AUTOMATED GEODATABASE UPDATING ASSISTED BY DEEP LEARNING." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B5-2021 (June 30, 2021): 13–19. http://dx.doi.org/10.5194/isprs-archives-xliii-b5-2021-13-2021.

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Abstract. The automation of geoinformation (GI) collection and interpretation has been a fundamental goal for many researchers. The developments in various sensors, platforms, and algorithms have been contributing to the achievement of this goal. In addition, the contributions of citizen science (CitSci) and volunteered geographical information (VGI) concepts have become evident and extensive for the geodata collection and interpretation in the era where information has the utmost importance to solve societal and environmental problems. The web- and mobile-based Geographical Information Systems (GIS) have facilitated the broad and frequent use of GI by people from any background, thanks to the accessibility and the simplicity of the platforms. On the other hand, the increased use of GI also yielded a great increment in the demand for GI in different application areas. Thus, new algorithms and platforms allowing human intervention are immensely required for semi-automatic GI extraction to increase the accuracy. By integrating the novel artificial intelligence (AI) methods including deep learning (DL) algorithms on WebGIS interfaces, this task can be achieved. Thus, volunteers with limited knowledge on GIS software can be supported to perform accurate processing and to make guided decisions. In this study, a web-based geospatial AI (GeoAI) platform was developed for map updating by using the image processing results obtained from a DL algorithm to assist volunteers. The platform includes vector drawing and editing capabilities and employs a spatial database management system to store the final maps. The system is flexible and can utilise various DL methods in the image segmentation.
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Kızılkaya, Serdar, Ugur Alganci, and Elif Sertel. "VHRShips: An Extensive Benchmark Dataset for Scalable Deep Learning-Based Ship Detection Applications." ISPRS International Journal of Geo-Information 11, no. 8 (August 10, 2022): 445. http://dx.doi.org/10.3390/ijgi11080445.

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The classification of maritime boats and ship targets using optical satellite imagery is a challenging subject. This research introduces a unique and rich ship dataset named Very High-Resolution Ships (VHRShips) from Google Earth images, which includes diverse ship types, different ship sizes, several inshore locations, and different data acquisition conditions to improve the scalability of ship detection and mapping applications. In addition, we proposed a deep learning-based multi-stage approach for ship type classification from very high resolution satellite images to evaluate the performance of the VHRShips dataset. Our “Hierarchical Design (HieD)” approach is an end-to-end structure that allows the optimization of the Detection, Localization, Recognition, and Identification (DLRI) stages, independently. We focused on sixteen parent ship classes for the DLR stages, and specifically considered eight child classes of the navy parent class at the identification stage. We used the Xception network in the DRI stages and implemented YOLOv4 for the localization stage. Individual optimization of each stage resulted in F1 scores of 99.17%, 94.20%, 84.08%, and 82.13% for detection, recognition, localization, and identification, respectively. The end-to-end implementation of our proposed approach resulted in F1 scores of 99.17%, 93.43%, 74.00%, and 57.05% for the same order. In comparison, end-to-end YOLOv4 yielded F1-scores of 99.17%, 86.59%, 68.87%, and 56.28% for DLRI, respectively. We achieved higher performance with HieD than YOLOv4 for localization, recognition, and identification stages, indicating the usability of the VHRShips dataset in different detection and classification models. In addition, the proposed method and dataset can be used as a benchmark for further studies to apply deep learning on large-scale geodata to boost GeoAI applications in the maritime domain.
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Bai, Yanbing, Erick Mas, and Shunichi Koshimura. "Towards Operational Satellite-Based Damage-Mapping Using U-Net Convolutional Network: A Case Study of 2011 Tohoku Earthquake-Tsunami." Remote Sensing 10, no. 10 (October 12, 2018): 1626. http://dx.doi.org/10.3390/rs10101626.

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The satellite remote-sensing-based damage-mapping technique has played an indispensable role in rapid disaster response practice, whereas the current disaster response practice remains subject to the low damage assessment accuracy and lag in timeliness, which dramatically reduces the significance and feasibility of extending the present method to practical operational applications. Therefore, a highly efficient and intelligent remote-sensing image-processing framework is urgently required to mitigate these challenges. In this article, a deep learning algorithm for the semantic segmentation of high-resolution remote-sensing images using the U-net convolutional network was proposed to map the damage rapidly. The algorithm was implemented within a Microsoft Cognitive Toolkit framework in the GeoAI platform provided by Microsoft. The study takes the 2011 Tohoku Earthquake-Tsunami as a case study, for which the pre- and post-disaster high-resolution WorldView-2 image is used. The performance of the proposed U-net model is compared with that of deep residual U-net. The comparison highlights the superiority U-net for tsunami damage mapping in this work. Our proposed method achieves the overall accuracy of 70.9% in classifying the damage into “washed away,” “collapsed,” and “survived” at the pixel level. In future disaster scenarios, our proposed model can generate the damage map in approximately 2–15 min when the preprocessed remote-sensing datasets are available. Our proposed damage-mapping framework has significantly improved the application value in operational disaster response practice by substantially reducing the manual operation steps required in the actual disaster response. Besides, the proposed framework is highly flexible to extend to other scenarios and various disaster types, which can accelerate operational disaster response practice.
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Rahman, Munshi Khaledur, Thomas W. Crawford, and Md Sariful Islam. "Shoreline Change Analysis along Rivers and Deltas: A Systematic Review and Bibliometric Analysis of the Shoreline Study Literature from 2000 to 2021." Geosciences 12, no. 11 (November 8, 2022): 410. http://dx.doi.org/10.3390/geosciences12110410.

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Globally, coastal zones, rivers and riverine areas, and deltas carry enormous values for ecosystems, socio-economic, and environmental perspectives. These often highly populated areas are generally significantly different from interior hinterlands in terms of population density, economic activities, and geophysical and ecological processes. Geospatial technologies are widely used by scholars from multiple disciplines to understand the dynamic nature of shoreline changes globally. In this paper, we conduct a systematic literature review to identify and interpret research patterns and themes related to shoreline change detection from 2000 to 2021. Two databases, Web of Science and Scopus, were used to identify articles that investigate shoreline change analysis using geospatial technique such as remote sensing and GIS analysis capabilities (e.g., the Digital Shoreline Analysis System (DSAS). Between the years 2000 and 2021, we initially found 1622 articles, which were inspected for suitability, leading to a final set of 905 articles for bibliometric analysis. For systematic analysis, we used Rayyan—a web-based platform used for screening literature. For bibliometric network analysis, we used the CiteSpace, Rayyan, and VOSviewer software. The findings of this study indicate that the majority of the literature originated in the USA, followed by India. Given the importance of protecting the communities living in the riverine areas, coastal zones, and delta regions, it is necessary to ask new research questions and apply cutting-edge tools and technology, such as machine learning approach and GeoAI, to fill the research gaps on shoreline change analysis. Such approaches could include, but are not limited to, centimeter level accuracy with high-resolution satellite imagery, the use of unmanned aerial vehicles (UAV), and point cloud data for both local and global level shoreline change and analysis.
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Valantiejus, Algimantas. "Ko reikia gerai interpretacijai?" Sociologija. Mintis ir veiksmas 16 (October 29, 2005): 136–43. http://dx.doi.org/10.15388/socmintvei.2005.2.6003.

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Rizaldi Makmur, Muhammad Rizaldi Makmur, and Alasman Mpesau. "Pengaruh Outlet Atmosphere dan Pelayanan Gerai terhadap Pembelian Implusif pada Gerai dalam Lippo Plaza Kota Kendari." Jurnal Ilmu Manajemen Sosial Humaniora (JIMSH) 2, no. 1 (January 23, 2021): 49–60. http://dx.doi.org/10.51454/jimsh.v2i1.28.

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Penelitian ini bertujuan untuk menguji dan menganalisis secara empiris pengaruh atmosphere dan pelayanan gerai terhadap pembelian impulsif pada gerai dalam Lippo Plaza Kendari. Penelitian ini bersifat explanatory yang bermaksud menjelaskan kedudukan variabel-variabel yang diteliti serta hubungan dan pengaruh antara satu variabel dengan variabel lain. Hasil penelitian menunjukkan bahwa: (1) Variabel atmosphere gerai dan pelayanan gerai berpengaruh positif terhadap pembelian impulsif pada gerai Lippo Plaza Kendari. Karena itu, hipotesis penelitian yang diajukan dapat diterima; (2) Variabel atmosphere gerai dan pelayanan gerai berpengaruh secara parsial terhadap pembelian impuls konsumen pada gerai Lippo Plaza Kendari; dan (3) Variabel pelayanan gerai merupakan variabel yang dominan mempengaruhi pembelian impuls pada gerai Lippo Plaza Kendari.
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38

Bacher, U. "HYBRID AERIAL SENSOR DATA AS BASIS FOR A GEOSPATIAL DIGITAL TWIN." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2022 (June 2, 2022): 653–59. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2022-653-2022.

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Abstract. More and more cities declare themselves to be a smart city or plan to be the same. Smart cities require a solid data source as basis for all further actions and the urban digital twin is the basis on which all information is collected and analysed. The urban digital twin is much more than just a 3D city model, but often this together with GIS data is the starting point for the urban digital twin. The basis of the urban digital twin is formed by geospatial data in the form of the geospatial digital twin. The digital twin hereby acts as a kind of hub into which all relevant and available information is included and analysed. To generate a geospatial digital twin aerial sensors that collect multiple data simultaneously, hybrid sensors, are perfectly suited for this task. In aerial data acquisition a new era started with the introduction of the first real hybrid sensor systems, like the Leica CityMapper-2. Hybrid in this context means the combination of an (oblique) camera system with a topographic LiDAR into an integrated aerial mapping system. By combining these complimentary sub-systems into one system the weaknesses of the one system could be compensated by using the alternative data source. An example is the mapping of low-light urban canyons, where image-based systems mostly produce unreliable results. For an LiDAR sensor the geometrical reconstruction of these areas is straight forward and leads to accurate results. The paper gives a detailed overview over the development and technical characteristics of hybrid sensor systems. The process of data acquisition is discussed and strategies for hybrid urban mapping are proposed. Furthermore, the paper provides insights into the advantage of LiDAR data for the 3D Mesh generation for urban modelling and on the possibilities to generate new products from the combination of the single products with the help of GeoAI. Finally, the use and some use cases of the hybrid sensor data and the derived products in the context of the urban digital twin is discussed and with the infinite loop of data, analysis, and action it is shown, that all data from the urban digital twin can only be a snapshot at a given point in time and the data recording and analysis is a permanent loop.
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Nguyen, Canh, Vasit Sagan, Sourav Bhadra, and Stephen Moose. "UAV Multisensory Data Fusion and Multi-Task Deep Learning for High-Throughput Maize Phenotyping." Sensors 23, no. 4 (February 6, 2023): 1827. http://dx.doi.org/10.3390/s23041827.

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Recent advances in unmanned aerial vehicles (UAV), mini and mobile sensors, and GeoAI (a blend of geospatial and artificial intelligence (AI) research) are the main highlights among agricultural innovations to improve crop productivity and thus secure vulnerable food systems. This study investigated the versatility of UAV-borne multisensory data fusion within a framework of multi-task deep learning for high-throughput phenotyping in maize. UAVs equipped with a set of miniaturized sensors including hyperspectral, thermal, and LiDAR were collected in an experimental corn field in Urbana, IL, USA during the growing season. A full suite of eight phenotypes was in situ measured at the end of the season for ground truth data, specifically, dry stalk biomass, cob biomass, dry grain yield, harvest index, grain nitrogen utilization efficiency (Grain NutE), grain nitrogen content, total plant nitrogen content, and grain density. After being funneled through a series of radiometric calibrations and geo-corrections, the aerial data were analytically processed in three primary approaches. First, an extended version normalized difference spectral index (NDSI) served as a simple arithmetic combination of different data modalities to explore the correlation degree with maize phenotypes. The extended NDSI analysis revealed the NIR spectra (750–1000 nm) alone in a strong relation with all of eight maize traits. Second, a fusion of vegetation indices, structural indices, and thermal index selectively handcrafted from each data modality was fed to classical machine learning regressors, Support Vector Machine (SVM) and Random Forest (RF). The prediction performance varied from phenotype to phenotype, ranging from R2 = 0.34 for grain density up to R2 = 0.85 for both grain nitrogen content and total plant nitrogen content. Further, a fusion of hyperspectral and LiDAR data completely exceeded limitations of single data modality, especially addressing the vegetation saturation effect occurring in optical remote sensing. Third, a multi-task deep convolutional neural network (CNN) was customized to take a raw imagery data fusion of hyperspectral, thermal, and LiDAR for multi-predictions of maize traits at a time. The multi-task deep learning performed predictions comparably, if not better in some traits, with the mono-task deep learning and machine learning regressors. Data augmentation used for the deep learning models boosted the prediction accuracy, which helps to alleviate the intrinsic limitation of a small sample size and unbalanced sample classes in remote sensing research. Theoretical and practical implications to plant breeders and crop growers were also made explicit during discussions in the studies.
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Norkus, Zenonas. "GERAI MODERUOTAS POKALBIS APIE KANTĄ." Problemos 67 (January 1, 2005): 139–42. http://dx.doi.org/10.15388/problemos.2005..4082.

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41

Humbel, René-Louis. "7e Colloque du GEAI." Revue Francophone des Laboratoires 2012, no. 444 (July 2012): 3. http://dx.doi.org/10.1016/s1773-035x(12)71516-2.

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Humbel, René-Louis. "8e Colloque du GEAI." Revue Francophone des Laboratoires 2014, no. 464 (July 2014): 3. http://dx.doi.org/10.1016/s1773-035x(14)72590-0.

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Humbel, René-Louis. "9e Colloque du GEAI." Revue Francophone des Laboratoires 2016, no. 484 (July 2016): 3. http://dx.doi.org/10.1016/s1773-035x(16)30236-2.

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Humbel, René-Louis. "5e Colloque du GEAI." Revue Francophone des Laboratoires 2008, no. 404 (July 2008): 3. http://dx.doi.org/10.1016/s1773-035x(08)71569-7.

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45

Setiawan, Ahmad, Tjipto Djuhartono, and Nur Sodik. "Pengaruh Kualitas Pelayanan Karyawan terhadap Kepuasan Pelanggan di Gerai Indomaret Kertamukti." Jurnal Arastirma 2, no. 1 (December 31, 2021): 116. http://dx.doi.org/10.32493/arastirma.v2i1.16853.

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Tujuan. Tujuan penelitian ini adalah untuk mengetahui pengaruh kualitas pelayanan karyawan terhadap kepuasan pelanggan di gerai Indomaret Kertamukti.Metode. Penelitian dilakukan dengan metode Asosiatif. Populasi adalah seluruh konsumen gerai Indomaret Kertamukti dengan besar sampel sebanyak 40 responden, dengan teknik sampling yang digunakan yaitu Purposive sampling. Instrumen yang digunakan berupa kuesioner sebanyak 30 pernyataan. Analisis data menggunakan regresi linier sederhana.Hasil. Hasil pengujian hipotesis diperoleh: Kualitas Pelayanan Karyawan berpengaruh positif dan signifikan terhadap Kepuasan Pelanggan di Gerai Indomaret Kertamukti, hal tersebut dapat dibuktikan dari nilai thitung 18,97 > ttabel 2,024. Hasil penelitian ini berguna untuk meningkatkan kepuasan pelanggan di gerai Indomaret Kertamukti melalui kualitas pelayanan karyawan.Implikasi. Perusahaan harus meningkatkan lagi kualitas pelayanan agar konsumen memiliki puncak pikiran terhadap produk atau jasa yang ditawarkan dan karyawan Indomaret Kertamukti harus berupaya memahami keinginan konsumen, sehingga konsumen selalu memilih Gerai Indomaret Kertamukti dari pada Gerai Indomaret lain.
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Raulinaitytė, Danguolė, Rasa Ugenskienė, Rasa Jančiauskienė, Elona Juozaitytė, and Laura Kairevičė. "Genai svarbūs storosios žarnos vėžio patogenezėje." Sveikatos mokslai 22, no. 5 (November 5, 2012): 77–84. http://dx.doi.org/10.5200/sm-hs.2012.101.

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Nugraha, Yoga Dwi, and Fitria Rachmawati. "SISTEM INFORMASI DATA CUSTOMER PADA GERAI AGEN BRI LINK BERBASIS WEB DI GERAI AGEN BRI LINK." INOVA-TIF 4, no. 1 (June 2, 2021): 20. http://dx.doi.org/10.32832/inova-tif.v4i1.5477.

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<p><em>Sistem komputerisasi merupakan salah satu alternatif yang paling banyak diminati dan digunakan oleh organisasi. Dalam sistem data customer. Sistem informasi mampu membantu dan mempermudah dalam menginput, memproses, menghasilkan bahkan menyimpan data. Sehingga penelitian ini bertujuan untuk membuat sistem informasi data customer pada gerai agen BRILink berbasis web. Pada penelititian ini dilakukan dalam 3 tahap terdiri dari tahap analisis sistem, tahap perancanga sistem dan database, dan yang terakhir tahap implementasi sistem. Hasil dari penelitian ini menghasilkan web Agen BRILink untuk pendaftaran customer baru secara online dan halaman control panel admin untuk mengelola customer baru dan lama. Dan system pendaftaran customer baru yang dapat diimplementasikan di Gerai Agen BRILink Dede Udin cabang Ciluar</em></p>
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Iskandar and Umar Tsani Abdurrahman. "PERANCANGAN APLIKASI KASIR POINT OF SALES BERBASIS ANDROID MENGGUNAKAN METODE RAPID APPLICATION DEVELOPMENT UNTUK USAHA RETAIL." INFOTECH : Jurnal Informatika & Teknologi 1, no. 2 (December 31, 2020): 67–77. http://dx.doi.org/10.37373/infotech.v1i2.62.

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Gerai merupakan tempat penjual menjajakan makanan atau minuman baik makanan ringan atau makanan berat untuk keperluan pangan sehari-hari. Dalam proses transaksi penjualan sebuah gerai dilakukan belum memanfaatkan teknologi seperti terdapat sebuah kasir dengan menggunakan sistem komputer. Sehingga dengan tidak menggunakan sebuah sistem kasir yang terkomputerisasi ini pengelola gerai banyak menemukan kendala dalam proses perhitungan dalam proses transaksinya diantaranya kesalahan hitung dan proses rekap transaksi yang relatif lama karena haru menghitung ulang setelah gerai tutup jualan sehingga perlu memerlukan kerja extra. Dalam mengatasi masalah tersebut perlu adanya aplikasi kasir untuk mengatasi kesalahan transaksi dalam perhitungan dan proses rekapitulasi, dalam analisis dan perancangan menggunakan metode UML sedangkan metode pengembangan perangkat lunak menggunakan metode Rapid Application Development (RAD) setelah dilakukan pengujian pada aplikasi kasir berbasis android tersebut menghasilkan performance yang cukup baik sehingga dapat digunakan untuk transaksi penjualan oleh pengelola gerai.
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Wati, Jasmin, and Farid Rusdi. "Employee Relations Dalam Isu Penutupan Perusahaan (Studi Kasus Gerai Matahari Di Mall Pluit Village)." Prologia 3, no. 1 (December 9, 2019): 135. http://dx.doi.org/10.24912/pr.v3i1.6229.

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Gerai Matahari adalah sebuah jaringan toko yang menjual baju dan pakaian lainnya. Dalam beberapa tahun terakhir Matahari marak dengan isu penutupan gerainya di Jakarta dan beberapa daerah di Indonesia. Employee Relations adalah sebuah proses mengatur dan memelihara hubungan harmonis antara pihak manajemen perusahaan dengan karyawan. Employee Relations sangat penting dilakukan karena dapat menciptakan iklim perusahaan yang sehat dan positif. Secara tidak langsung kegiatan employee relations dapat mempengaruhi hubungan yang harmonis antara perusahaan dan karyawan. Tujuan penelitian ini adalah untuk mengetahui bagaimana kegiatan employee relations dalam menangani isu penutupan gerai matahari tersebut. Teori yang digunakan dalam penelitian ini adalah Employee Relations dan Public Relations. Metode penelitian yang digunakan dalam penelitian ini adalah kualitatif dengan mewawancarai Store Manager, Assistant Store Manager dan Personalia Supervisor Gerai Matahari Pluit Village. Hasil temuan dari penelitian ini menunjukkan bahwa kegiatan employee relations berperan penting dalam membangun hubungan baik karyawan Gerai Matahari Mall Pluit Village. Hal ini bisa memberi ketenangan bagi internal karyawan di tengah isu penutupan Gerai Matahari.
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Chaery, Ahmad Ridho Fachrizal, Jaenal Abidin, Jarno Jarno, Budi Tri Santoso, and Ayumi Rahma. "Sosialisasi Implementasi Sak Emkm pada UMKM yang tergabung di Gerai Lengkong." DEDIKASI PKM 3, no. 2 (May 1, 2022): 270. http://dx.doi.org/10.32493/dedikasipkm.v3i2.20197.

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Kegiatan Pengabdian Kepada Masyarakat (PKM) ini bekerja sama dengan Gerai Lengkong. Tujuan PKM ini adalah untuk membekali para pelaku UMKM yang tergabung di Gerai Lengkong agar dapat menerapkan prinsip-prinsip akuntansi dalam kegiatan bisnisnya. Hal ini diharapkan dapat meningkatkan usaha dan kesejahteraan pelaku UMKM. Gerai Lengkong merupakan pusat oleh-oleh produk UMKM Tangerang Selatan yang terletak di Kecamatan Serpong, Kota Tangerang Selatan, Banten. Dengan adanya Gerai Lengkong, diharapkan UMKM dapat memiliki pangsa pasar yang lebih luas dan tidak hanya sekedar di daerah Tangsel saja. Gerai lengkong diharapkan dapat menjadi garis depan perjuangan UMKM. Pelaksanaan kegiatan dengan metode memberikan pemaparan yang disertai dengan diskusi interaktif secara langsung antara kelompok dosen pelaksana PKM dan peserta. Pemberian materi dilakukan diawal pertemuan selama pelaksanaan. Berdasarkan hasil pengamatan dan interview secara sampling terhadap peserta, dapat disimpulkan bahwa pelaksanaan PKM memberikan wawasan baru bagi peserta dalam implementasi SAK EMKM dalam penyusunan laporan keuangan sekaligus meningkatkan kedisplinan pelaku UMKM terhadap pencatatan akuntansi. Semua peserta antusias mengikuti acara hingga selesai dan merasakan manfaatpelatihan bagi kemajuan usaha mereka.
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