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Zeitschriftenartikel zum Thema „Land usage classification“

Autor: Grafiati

Veröffentlicht am 7. Juni 2025

Zuletzt aktualisiert am 2. August 2025

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PALOMO, E. J., D. ELIZONDO, and G. BRUNSCHWIG. "Land usage classification: a hierarchical neural network approach." Journal of Agricultural Science 152, no. 5 (2013): 817–28. http://dx.doi.org/10.1017/s0021859613000737.

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SUMMARYThe classification of land usage in mountain grassland bovine areas is important for the management of forage production and grazing in grass-based livestock systems. The present paper proposes a novel, hierarchical neural network-based approach towards the classification of land usage in these areas. A survey of 72 farms was conducted in the Massif Central (France). Information was gathered on geographical characteristics and cutting and/or grazing practices on three general groups of fields: cut only, cut and grazed and grazed only fields. To classify land usage, the data were cluster

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Khurum, Nazir Junejo. "A novel pixel-based supervised hybrid approach for prediction of land cover from satellite imagery." Indian Journal of Science and Technology 13, no. 17 (2020): 1786–94. https://doi.org/10.17485/IJST/v13i17.538.

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Abstract <strong>Background/Objectives:</strong> To determine the land use/cover from satellite imagery using image enhancement, image processing, and supervised machine learning techniques. This land usage will help in land use policy development, disaster assessment, planning of urban infrastructure, forest and agriculture monitoring and conservation. <strong>Methods/Statistical analysis:</strong> A pixel-based supervised hybrid machine learning approach is used that combines parametric density estimation followed by a k-nearest neighbor (k-NN) classifier to predict whether a

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Semochkin, V. N., Z. N. Bakanova, V. P. Rodionov, and A. N. Shurukhina. "Non-usage of land: causes and solutions." Zemleustrojstvo, kadastr i monitoring zemel' (Land management, cadastre and land monitoring), no. 6 (May 22, 2023): 331–36. http://dx.doi.org/10.33920/sel-04-2306-02.

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The article discusses the issues related to the problem of non-usage of agricultural land. In modern conditions of the agro–industrial complex development in the country, the restoration of unused lands can increase land potential of agriculture. The reasons and classification of the non-usage of land are carried out in this paper. The authors consider the basic requirements for solving the problem.

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Meng, Y., Y. Cao, H. Tian, and Z. Han. "THE IDENTIFICATION OF LAND UTILIZATION IN COASTAL RECLAMATION AREAS IN TIANJIN USING HIGH RESOLUTION REMOTE SENSING IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 1275–77. http://dx.doi.org/10.5194/isprs-archives-xlii-3-1275-2018.

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In recent decades, land reclamation activities have been developed rapidly in Chinese coastal regions, especially in Bohai Bay. The land reclamation areas can effectively alleviate the contradiction between land resources shortage and human needs, but some idle lands that left unused after the government making approval the usage of sea areas are also supposed to pay attention to. Due to the particular features of land coverage identification in large regions, traditional monitoring approaches are unable to perfectly meet the needs of effectively and quickly land use classification. In this pa

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Burai, Péter. "Usage of different remote sensing data in land use and vegetation monitoring…………………………" Acta Agraria Debreceniensis, no. 22 (May 23, 2006): 7–12. http://dx.doi.org/10.34101/actaagrar/22/3178.

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The use of remote sensing in forest management and agriculture is becoming more prominent. The rapid development of technology allowed the emergence of database suitable for precision application in addition to the previously used low-resolution and low data content images. The high resolution, hyperspectral images are not only suitable for separating the different land use categories and vegetation types but also for examining the soil characteristics and biophysical features of plants (Blackburn and Steel, 1999; Condit, 1970). We processed a multispectral satellite image (Landsat 7 ETM+) and

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Gonçalves, Vitor da Silva, Italo de Oliveira Matias, and Aldo Shimoya. "Understanding the usage of land use and land cover classifiers in scientific research." Revista Produção e Desenvolvimento 9, no. 1 (2023): e660. http://dx.doi.org/10.32358/rpd.2023.v9.660.

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Purpose: assess the primary methods utilized in land use and land cover classification research to determine the most frequently applied techniques and potential trends Methodology/Approach: a bibliometric study was carried out in the Scopus scientific article databases, counting the use of classification methods between 2012 and 2022. The obtained data was converted into SQL database tables and processed using queries, looking for articles whose abstracts contains keywords related to land cover and land use methods. Findings: a general growth trend in the studies of this area was

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J. W. Rogers and S. F. Shih. "Land Use Classification in Agricultural Water Usage Permitting Program Via Landsat Data." Applied Engineering in Agriculture 6, no. 1 (1990): 54–58. http://dx.doi.org/10.13031/2013.26344.

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Fulan, Mateus Henrique, Wanderson Cleiton Schmidt Cavalheiro, Nilson Reinaldo Fernandes dos Santos Junior, et al. "Morphometric Transformations of the Perdizes and Fojo Watersheds in Campos do Jordão, SP, Brazil: Land Use/Land Cover Changes after 12 Years, with Focus on Urbanized Areas." Revista de Gestão Social e Ambiental 18, no. 8 (2024): e07096. http://dx.doi.org/10.24857/rgsa.v18n8-143.

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Objective: The main objective of this study was to compare urbanized areas, supplemented by additional information such as tree vegetation, areas without vegetation, and the presence of agriculture in the Fojo and Perdizes watershed located in Campos do Jordão, SP, Brazil, 12 years after their characterization, using supervised classification methods. Theoretical Framework: The use of geotechnologies for the assessment of the LULC change allows the holistic visualization of the area, compared to former studies. Method: The base image for the study was generated through the Semi-Automatic Class

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Ninad More. "A Complete Study of Remote Sensing- Sentinel-2 Satellite Data for Land Use / Land Cover (LULC) Analysis." Panamerican Mathematical Journal 35, no. 1s (2024): 231–49. http://dx.doi.org/10.52783/pmj.v35.i1s.2311.

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Remote sensing based Satellite collected Images is a complete advanced process of both the automatic detecting observation and overall earth monitoring as well as observing the mainly physical level of characteristics of an area by mainly measuring its reflected and emitted radiation at a distance mainly used from both satellite and aircraft. Special quality cameras collect remotely based sensed images, which help all researchers "sense" things about overall Earth observation. The European Research Space Agency (ESA) and the European (member countries) Union-EU both have equally provided toget

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Nguyen, Thao-Ngan, and Van-Ho Nguyen. "Agricultural Land-Use Classification on Satellite Data Using Machine Learning." Business Systems Research Journal 16, no. 1 (2025): 219–32. https://doi.org/10.2478/bsrj-2025-0011.

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Abstract Background The utilization of satellite images has become increasingly popular for detecting land usage, focusing on agricultural land classification in recent years, due to the significant decline in bees. Objectives This paper seeks to address these challenges by applying several machine learning algorithms on multi-spectral satellite data from Sentinel-2 to derive accurate land classification models. Methods/Approach Specifically, we use five bands: Red, Green, Blue, NIR, and NDVI to build three models, namely Random Forest (RF), Convolutional Neural Network (CNN), and Long Short-T

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Kandissounon, Gilles A., Ajay Kalra, and Sajjad Ahmad. "Integrating System Dynamics and Remote Sensing to Estimate Future Water Usage and Average Surface Runoff in Lagos, Nigeria." Civil Engineering Journal 4, no. 2 (2018): 378. http://dx.doi.org/10.28991/cej-030998.

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The goal of this study was twofold; first analyze the patterns of water consumption in Lagos, Nigeria and use them in a System Dynamics (SD) model to make projections about future demand. The second part used remote sensing to quantify the contribution of extensive land use/cover change to urban flooding. Land use/cover dynamics over the past decade was analyzed using satellite imagery provided by Landsat Thematic Mapping (TM). Unsupervised classification was performed with false color composite using the Iterative Self-Organizing Data Analysis (ISODATA) technique in a Geographic Information S

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Pratomo, Jati, and Triyoga Widiastomo. "IMPLEMENTATION OF THE MARKOV RANDOM FIELD FOR URBAN LAND COVER CLASSIFICATION OF UAV VHIR DATA." Geoplanning: Journal of Geomatics and Planning 3, no. 2 (2016): 127. http://dx.doi.org/10.14710/geoplanning.3.2.127-136.

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The usage of Unmanned Aerial Vehicle (UAV) has grown rapidly in various fields, such as urban planning, search and rescue, and surveillance. Capturing images from UAV has many advantages compared with satellite imagery. For instance, higher spatial resolution and less impact from atmospheric variations can be obtained. However, there are difficulties in classifying urban features, due to the complexity of the urban land covers. The usage of Maximum Likelihood Classification (MLC) has limitations since it is based on the assumption of the normal distribution of pixel values, where, in fact, urb

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Raguraman, Naren, and Ashutosh Bhardwaj. "Spatial Analysis of Land Use Land Cover Dynamics in the Madurai District Using Sentinel-2 Data and Supervised Learning Algorithms." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4-2024 (October 21, 2024): 367–72. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-2024-367-2024.

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Abstract. LULC, or Land Use and Land Cover, refers to the classification and description of different types of land and its usage patterns, including urban areas, forests, agricultural land, etc. In remote sensing, satellite imagery for LULC mapping is becoming more widespread. Numerous studies examine various approaches to improve mapping efficiency and accuracy, highlighting the significance of various data sources, machine learning algorithms, and categorization techniques. This study employs machine learning classifiers, namely Random Forest (RF), Support Vector Machine (SVM), Gradient Boo

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Gu, H. Y., H. T. Li, Z. Y. Liu, and C. Y. Shao. "A SEMI-AUTOMATIC RULE SET BUILDING METHOD FOR URBAN LAND COVER CLASSIFICATION BASED ON MACHINE LEARNING AND HUMAN KNOWLEDGE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 13, 2017): 729–32. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-729-2017.

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Classification rule set is important for Land Cover classification, which refers to features and decision rules. The selection of features and decision are based on an iterative trial-and-error approach that is often utilized in GEOBIA, however, it is time-consuming and has a poor versatility. This study has put forward a rule set building method for Land cover classification based on human knowledge and machine learning. The use of machine learning is to build rule sets effectively which will overcome the iterative trial-and-error approach. The use of human knowledge is to solve the shortcomi

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Koeva, Mila, Rohan Bennett, and Claudio Persello. "Remote Sensing for Land Administration 2.0." Remote Sensing 14, no. 17 (2022): 4359. http://dx.doi.org/10.3390/rs14174359.

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Contemporary land administration (LA) systems incorporate the concepts of cadastre and land registration. Conceptually, LA is part of a global land management paradigm incorporating LA functions such as land value, land tenure, land development, and land use. The implementation of land-related policies integrated with well-maintained spatial information reflects the aim set by the United Nations to deliver tenure security for all (Sustainable Development Goal target 1.4, amongst many others). Innovative methods for data acquisition, processing, and maintaining spatial information are needed in

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Kimtai, James Kwemoi, Joash W. S. Mabonga, and Nalyanya Wasike. "The Trend of Peri-Urban Land Use Change in Small and Medium Sized Urban Areas. A Case of Kimilili Town, Bungoma County, Kenya." African Journal of Empirical Research 4, no. 2 (2023): 1338–44. http://dx.doi.org/10.51867/ajernet.4.2.134.

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The purpose of this study was to determine patterns in land use change in Kimilili peri-urban areas between 1990 and 2020. We got Landsat data for the years 1990, 2000, 2010, and 2020 from the USGS website. Arc GIS version 10 and ERDAS Imagine version 2018 were used for digital image processing and GIS analysis. Imageries for the following land use groups were obtained using supervised classification using the Maximum Likelihood classification algorithm: built-up area, agricultural land, dense vegetation, and bare terrain. Ground truthing was done in order to get the required reference informa

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Atik, Saziye Ozge, and Cengizhan Ipbuker. "Integrating Convolutional Neural Network and Multiresolution Segmentation for Land Cover and Land Use Mapping Using Satellite Imagery." Applied Sciences 11, no. 12 (2021): 5551. http://dx.doi.org/10.3390/app11125551.

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Depletion of natural resources, population growth, urban migration, and expanding drought conditions are some of the reasons why environmental monitoring programs are required and regularly produced and updated. Additionally, the usage of artificial intelligence in the geospatial field of Earth observation (EO) and regional land monitoring missions is a challenging issue. In this study, land cover and land use mapping was performed using the proposed CNN–MRS model. The CNN–MRS model consisted of two main steps: CNN-based land cover classification and enhancing the classification with spatial f

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Szostak, Marta. "Usage PlanetScope Images and LiDAR Point Clouds for Characterizing the Forest Succession Process in Post-Agricultural Areas." Sustainability 14, no. 21 (2022): 14110. http://dx.doi.org/10.3390/su142114110.

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The paper investigates using PlanetScope satellite images together with LiDAR data for automation of land use/cover (LULC) mapping and 3D vegetation characteristics in the aspect of mapping and monitoring of the secondary forest succession areas. The study was performed for the tested area in the Biskupice district (South of Poland), where a forest succession occurs on post-agricultural lands. The research area was parcels where the forest overgrowing process was identified. It was verified whether the image processing allows for reliable LULC classification as an identification forest success

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Fayaz, Muhammad, Junyoung Nam, L. Minh Dang, Hyoung-Kyu Song, and Hyeonjoon Moon. "Land-Cover Classification Using Deep Learning with High-Resolution Remote-Sensing Imagery." Applied Sciences 14, no. 5 (2024): 1844. http://dx.doi.org/10.3390/app14051844.

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Land-area classification (LAC) research offers a promising avenue to address the intricacies of urban planning, agricultural zoning, and environmental monitoring, with a specific focus on urban areas and their complex land usage patterns. The potential of LAC research is significantly propelled by advancements in high-resolution satellite imagery and machine learning strategies, particularly the use of convolutional neural networks (CNNs). Accurate LAC is paramount for informed urban development and effective land management. Traditional remote-sensing methods encounter limitations in precisel

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Kumar Musali, Suresh, Rajeshwari Janthakal, and Nuvvusetty Rajasekhar. "Holdout based blending approaches for improved satellite image classification." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 3 (2024): 3127. http://dx.doi.org/10.11591/ijece.v14i3.pp3127-3136.

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An essential component of remote sensing, image analysis, and pattern recognition is image categorization. The classification of land use using remotely sensed data creates a map-like representation as the final form of the investigation. With its ability to effectively categorize satellite images, machine learning (ML) algorithms have gained significant traction in a number of fields, including land-use planning, disaster response, and natural resource management. Ensemble learning is also a widely used technique for enhancing the precision of satellite image categorization, which combines mu

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Dong, Lin, Jiazi Li, Yingjun Xu, Youtian Yang, Xuemin Li, and Hua Zhang. "Study on the Spatial Classification of Construction Land Types in Chinese Cities: A Case Study in Zhejiang Province." Land 10, no. 5 (2021): 523. http://dx.doi.org/10.3390/land10050523.

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Identifying the land-use type and spatial distribution of urban construction land is the basis of studying the degree of exposure and the economic value of disaster-affected bodies, which are of great significance for disaster risk predictions, emergency disaster reductions, and asset allocations. Based on point of interest (POI) data, this study adopts POI spatialization and the density-based spatial clustering of applications with noise (DBSCAN) algorithm to accomplish the spatial classification of construction land. Zhejiang province is selected as a study area, and its construction land is

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Akbar, M. S., M. H. Sarker, M. A. Sattar, et al. "INTEGRATED USE OF REMOTE SENSING, GIS AND GPS TECHNOLOGY FOR MONITORING THE ENVIRONMENTAL PROBLEM OF SHYAMNAGAR." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1/W1 (May 31, 2017): 357–64. http://dx.doi.org/10.5194/isprs-archives-xlii-1-w1-357-2017.

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Cultivation of shrimp mostly in unplanned way has been considered as one of the major environmental disasters of Shamnagar. Villagers surrounding the rivers are mainly involved with fish (shrimp) cultivation. So, fertile agriculture land has been converted to shrimp cultivation. Conversion of agriculture land to other usage is a common but acute problem for land resources of the country like Bangladesh. Conventional methods for collecting this information are relatively costly and time consuming. Contrarily, Remote Sensing satellite observation with its unique capability to provide cost-effect

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Abdul Karim, Muhammad Firdaus, Marinah Muhammad, Noor Janatun Naim Jemali, and Arham Muchtar Achmad Bahar. "Assessing the Dynamics of Urban Growth and Land Use Changes in Jeli Using Geospatial Technique." Journal of Tropical Resources and Sustainable Science (JTRSS) 8, no. 1 (2021): 28–35. http://dx.doi.org/10.47253/jtrss.v8i1.161.

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Land use change pattern in Jeli is been given a focus as part of tool for land planning anddevelopment. An increasing of population in Jeli make this study is relevant to aid an understandingon land use changes in this area due to the demanded for development and rapid land utilisation.Land use change pattern can be obtained via geospatial technique by Geographical InformationSystem (GIS) together with satellite imagery analysis. In this study, land use maps produced fromsupervised classification using maximum likelihood algorithm give a high accuracy of 92.05%.From classified land use images,

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Voelsen, M., S. Lauble, F. Rottensteiner, and C. Heipke. "TRANSFORMER MODELS FOR MULTI-TEMPORAL LAND COVER CLASSIFICATION USING REMOTE SENSING IMAGES." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-1/W1-2023 (December 5, 2023): 981–90. http://dx.doi.org/10.5194/isprs-annals-x-1-w1-2023-981-2023.

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Abstract. The pixel-wise classification of land cover, i.e. the task of identifying the physical material of the Earth’s surface in an image, is one of the basic applications of satellite image time series (SITS) processing. With the availability of large amounts of SITS it is possible to use supervised deep learning techniques such as Transformer models to analyse the Earth’s surface at global scale and with high spatial and temporal resolution. While most approaches for land cover classification focus on the generation of a mono-temporal output map, we extend established deep learning models

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Khan, Sheeba, Ashutosh Bhardwaj, and Mathivanan Sakthivel. "Accuracy Assessment of Land Use Land Cover Classification Using Machine Learning Classifiers in Google Earth Engine; A Case Study of Jammu District." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4-2024 (October 21, 2024): 263–68. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-2024-263-2024.

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Abstract. LULC (Land Use and Land Cover) involves classifying and describing different land types and their usage. Using satellite imagery for LULC mapping is increasing in remote sensing. This study focuses on Jammu district in India, situated between mountain ranges from north and south make it eco-sensitive zone. Expanding of human activity and loss of natural resources make it vulnerable if mismanaged. Study of LULC is important because of it and this study deals with efficiency and accuracy of various machine learning classifiers for LULC. This study uses machine learning classifiers - Ra

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Kumar, Pramendra. "General Land Use Pattern In Ghaziabad District, Uttar Pradesh, India: A Detailed Analysis." Innovation The Research Concept 9, no. 1 (2024): E 108 — E 113. https://doi.org/10.5281/zenodo.10836836.

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This paper has been published in Peer-reviewed International Journal "Innovation The Research Concept"     URL : https://www.socialresearchfoundation.com/new/publish-journal.php?editID=8681 Publisher : Social Research Foundation, Kanpur (SRF International)     Abstract : Land usage refers to surface exploitation of advanced and undeveloped land on a given point in time and location. The two most likely causes of this alteration are. First, a change in the usage of the land may be brought about by societal demands. Second, the effects of technology also e

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Elmogiera Elawad, Muzzamil Atta, Mohammed Agied, Mohamed Ahmed, Hussein Alyahri, and Mohamed Elbashir. "Livestock Practices: Traditional Animal Holdings Classification in Qatar 2020 Towards Sustainable Food Security." Journal of Advanced Zoology 44, no. 4 (2023): 409–18. http://dx.doi.org/10.17762/jaz.v44i4.1941.

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Background: Traditional animal holdings (TAH) in Qatar face many managerial challenges, such as inadequate usage of land capacity, low levels of animal productivity and low economic returns. The top priority of the Ministry of Municipality strategy is to take care of TAH to ensure the sustainability of this activity and to maximize its role in national food security. To support future policy choices and services provision, the ministry initiated a TAH classification system. In 2020, the Social and Economic Survey Research Institute (SESRI) of Qatar University conducted a comprehensive agricult

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Mendoza-Hurtado, Manuel, Gonzalo Cerruela-García, and Domingo Ortiz-Boyer. "A Supervised Approach for Land Use Identification in Trento Using Mobile Phone Data as an Alternative to Unsupervised Clustering Techniques." Applied Sciences 15, no. 4 (2025): 1753. https://doi.org/10.3390/app15041753.

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This study explores land use classification in Trento using supervised learning techniques combined with call detail records (CDRs) as a proxy for human activity. Located in an alpine environment, Trento presents unique geographic challenges, including varied terrain and sparse network coverage, making it an ideal case for testing the robustness of supervised learning approaches. By analyzing spatiotemporal patterns in CDRs, we trained and evaluated several classification algorithms, including k-nearest neighbors (kNN), support vector machines (SVM), and random forests (RF), to map land use ca

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Shivanagi, Chetana, and Shivayogi B. Ullagaddi. "Machine Learning in Agricultural Soil Analysis: A Comprehensive Review on Classification and Fertility Prediction." International Journal of Engineering Research and Applications 15, no. 6 (2025): 95–103. https://doi.org/10.9790/9622-150695103.

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The role of soil classification in agriculture has become increasingly vital with technological progress. Traditional soil classification methods typically involve laborious tasks like manual sampling, visual assessments, and basic lab tests. These approaches can be time-intensive, expensive, and occasionally less precise, making them less ideal for contemporary agricultural demands, particularly with the need for enhanced productivity and precision. Technological advancements have introduced new methods such as remote sensing, machine learning, and artificial intelligence, revolutionizing soi

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Zhao, Bingbing, Xiaoyong Tan, Liang Luo, Min Deng, and Xuexi Yang. "Identifying the Production–Living–Ecological Functional Structure of Haikou City by Integrating Empirical Knowledge with Multi-Source Data." ISPRS International Journal of Geo-Information 12, no. 7 (2023): 276. http://dx.doi.org/10.3390/ijgi12070276.

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The inefficient use of urban resources and the imbalance of spatial structures make optimizing land use management a top priority in urban environmental management. Traditional land use classification systems that prioritize only natural features while disregarding human activity can result in redundancy and conflicts in urban planning. The Production–Living–Ecological Space (PLES) approach was developed as an integrated method for territorial spatial classification. However, most existing studies on PLES are conducted at provincial scales, largely overlooking fine-scale usage within cities. I

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Voelsen, M., M. Teimouri, F. Rottensteiner, and C. Heipke. "INVESTIGATING 2D AND 3D CONVOLUTIONS FOR MULTITEMPORAL LAND COVER CLASSIFICATION USING REMOTE SENSING IMAGES." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2022 (May 17, 2022): 271–79. http://dx.doi.org/10.5194/isprs-annals-v-3-2022-271-2022.

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Abstract. With the availability of large amounts of satellite image time series (SITS), the identification of different materials of the Earth’s surface is possible with a high temporal resolution. One of the basic tasks is the pixel-wise classification of land cover, i.e. the task of identifying the physical material of the Earth’s surface in an image. Fully convolutional neural networks (FCN) are successfully used for this task. In this paper, we investigate different FCN variants, using different methods for the computation of spatial, spectral, and temporal features. We investigate the imp

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Radman, Ali, Fariba Mohammadimanesh, and Masoud Mahdianpari. "Wet-ConViT: A Hybrid Convolutional–Transformer Model for Efficient Wetland Classification Using Satellite Data." Remote Sensing 16, no. 14 (2024): 2673. http://dx.doi.org/10.3390/rs16142673.

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Accurate and efficient classification of wetlands, as one of the most valuable ecological resources, using satellite remote sensing data is essential for effective environmental monitoring and sustainable land management. Deep learning models have recently shown significant promise for identifying wetland land cover; however, they are mostly constrained in practical issues regarding efficiency while gaining high accuracy with limited training ground truth samples. To address these limitations, in this study, a novel deep learning model, namely Wet-ConViT, is designed for the precise mapping of

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Syauqy, Dahnial, Hurriyatul Fitriyah, and Khairul Anwar. "Classification of Physical Soil Condition for Plants using Nearest Neighbor Algorithm with Dimensionality Reduction of Color and Moisture Information." Journal of Information Technology and Computer Science 3, no. 2 (2018): 175. http://dx.doi.org/10.25126/jitecs.20183266.

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Determining the quality of soil is an important task to perform especially on newly opened agricultural land since it may provide significant impact on the growth of plants. One alternative to determine physical soil quality is by visually observe the color of the soil and measure its moisture. This paper designed an embedded system classify soil condition for plants according to the dimensionality reduction of color and moisture information from the soil using k-NN algorithm. The dimension of attribute information was reduced using correlation analysis to achieve lower computational time and

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Grewal, Reaya, Singara Singh Kasana, and Geeta Kasana. "Machine Learning and Deep Learning Techniques for Spectral Spatial Classification of Hyperspectral Images: A Comprehensive Survey." Electronics 12, no. 3 (2023): 488. http://dx.doi.org/10.3390/electronics12030488.

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The growth of Hyperspectral Image (HSI) analysis is due to technology advancements that enable cameras to collect hundreds of continuous spectral information of each pixel in an image. HSI classification is challenging due to the large number of redundant spectral bands, limited training samples and non-linear relationship between the collected spatial position and the spectral bands. Our survey highlights recent research in HSI classification using traditional Machine Learning techniques like kernel-based learning, Support Vector Machines, Dimension Reduction and Transform-based techniques. O

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Kumar, Musali Suresh, Rajeshwari Janthakal, and Nuvvusetty Rajasekhar. "Holdout based blending approaches for improved satellite image classification." Holdout based blending approaches for improved satellite image classification 14, no. 3 (2024): 3127–36. https://doi.org/10.11591/ijece.v14i4.pp3127-3136.

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An essential component of remote sensing, image analysis, and pattern recognition is image categorization. The classification of land use using remotely sensed data creates a map-like representation as the final form of the investigation. With its ability to effectively categorize satellite images, machine learning (ML) algorithms have gained significant traction in a number of fields, including land-use planning, disaster response, and natural resource management. Ensemble learning is also a widely used technique for enhancing the precision of satellite imag

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Fatiawan, Engki, Hazairin Zubair, and Syamsul Arifin Lias. "Pengaruh Perubahan Tata Guna Lahan Terhadap Kondisi Tata Air Daerah Aliran Sungai (DAS) Tallo." Jurnal Ecosolum 13, no. 2 (2024): 179–99. https://doi.org/10.20956/ecosolum.v13i2.33319.

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Land use conversion affects the water management of a watershed, leading to flooding, erosion, and sedimentation. Tallo Watershed located in South Sulawesi is considered critical due to frequent flooding. This study aims to change the land use of the Tallo River Watershed (DAS) and its influence on watershed performance in terms of water management aspects. The methods used include spatial analysis with supervised classification to examine land cover changes and the Ministry of Forestry Regulation No. P61 of 2014 for water management evaluation. The largest land use conversion occurred in padd

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Tapia, M. N., and M. V. B. Morais. "LAND USE DATA IN THE MIDDLE MAULE RIVER SUB-BASIN: CLASSIFICATION AND COMPARISON BETWEEN 1999 AND 2019." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W12-2020 (November 6, 2020): 545–50. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w12-2020-545-2020.

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Abstract. The use of satellite images is a modern strategy for the evaluation and prediction of various weather scenarios. In addition, this is a key tool for the development of environmental sciences. Since the end of the last decade, Chile has been suffering from a megadrought associated with climate change. In this context, this study proposes to evaluate the role of land use change in the Middle Maule River sub-basin, located in the Maule Region, Chile. This is an important sector characterized by a significant agricultural and hydroelectric contribution. To do so, this study performs a su

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Padarlan, Padarlan, Jamal Harimudin, Sawaludin Sawaludin, Tahir Tahir, and Saban Rahim. "Analisis Pola Perubahan Penggunaan Lahan Menggunakan Google Earth Engine Di Kecamatan Wawonii Barat." JAGAT (Jurnal Geografi Aplikasi dan Teknologi) 8, no. 1 (2024): 50. https://doi.org/10.33772/jagat.v8i1.41001.

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Konawe Island Regency was formed in 2013 as a new autonomous entity, with West Wawonii District serving as its capital. As a result, developments in the West Wawonii District have impacted agricultural and non-agricultural land use changes. The study's objectives were to determine land usage in 2011 and 2022, as well as analyze land use patterns from 2011 through 2022. The study made use of SHP Konawe Islands Regency data, BPS data for 2012 and 2022, Landsat 7 and 9 photos, and RBI maps. The Classification And Regression Tree (CART) method is used in this study, which is conducted through the

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Smreczak, Bożena, and Andrzej Łachacz. "Soil types specified in the bonitation classification and their analogues in the sixth edition of the Polish Soil Classification." Soil Science Annual 70, no. 2 (2019): 115–36. http://dx.doi.org/10.2478/ssa-2019-0011.

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Abstract The aim of the paper was to present the correlation between soil types specified in the sixth edition of the Polish Soil Classification (SGP6 2019) and Polish bonitation classification. The comparisons included two categories of agricultural land: arable soils and soils of permanent grasslands. In Poland bonitation maps are one of the oldest documentations regarding soil cover. They were elaborated in an uniform manner and based on the field examination of soil profiles. Soil information reflected specific rules adopted in the soil quality classification, including identification of s

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Siregar, Enni Sari. "Shaping a Sustainable Future: How Energy Consumption and Carbon Emissions Drive Low-Carbon Development." Signifikan: Jurnal Ilmu Ekonomi 14, no. 1 (2025): 93–110. https://doi.org/10.15408/sjie.v14i1.44779.

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Research Originality: The study examines the impact of deforestation, energy use, transportation, and industrialization in North Sumatra from 1991 to 2021 on low-carbon development. It aims to understand environmental change drivers and propose strategies to mitigate their negative effects on development.Research Objectives: This research aims to explore the relationship between deforestation, energy consumption, land transportation, and industrialization as factors influencing low-carbon development.Research Methods: The study examines factors influencing low-carbon development in North Sumat

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Cui, Wenchao, Yanjun Chen, and Hengyuan Zeng. "Can Internet Use Narrow the Gap between Farmers’ Willingness and Behavior in Waste Classification? Empirical Evidence from Rural Areas in Jiangsu Province, China." Sustainability 16, no. 7 (2024): 2726. http://dx.doi.org/10.3390/su16072726.

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Household waste classification and treatment are important for environmental protection and sustainable development. The Logit model is used to analyze differences in farmers’ willingness and behavior regarding waste classification based on data from the China Land Economic Survey. Key findings include the following: (1) There is an evident discrepancy between waste classification willingness and action among rural residents. Despite nearly 90% of the sampled farmers expressing a willingness, nearly 40% do not practice waste classification. (2) Internet usage significantly reduces the discrepa

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Voelsen, M., D. Lobo Torres, R. Q. Feitosa, F. Rottensteiner, and C. Heipke. "INVESTIGATIONS ON FEATURE SIMILARITY AND THE IMPACT OF TRAINING DATA FOR LAND COVER CLASSIFICATION." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2021 (June 17, 2021): 181–89. http://dx.doi.org/10.5194/isprs-annals-v-3-2021-181-2021.

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Abstract. Fully convolutional neural networks (FCN) are successfully used for pixel-wise land cover classification - the task of identifying the physical material of the Earth’s surface for every pixel in an image. The acquisition of large training datasets is challenging, especially in remote sensing, but necessary for a FCN to perform well. One way to circumvent manual labelling is the usage of existing databases, which usually contain a certain amount of label noise when combined with another data source. As a first part of this work, we investigate the impact of training data on a FCN. We

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Hecht, Robert, Matthias Kalla, and Tobias Krüger. "Crowd-sourced data collection to support automatic classification of building footprint data." Proceedings of the ICA 1 (May 16, 2018): 1–7. http://dx.doi.org/10.5194/ica-proc-1-54-2018.

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Human settlements are mainly formed by buildings with their different characteristics and usage. Despite the importance of buildings for the economy and society, complete regional or even national figures of the entire building stock and its spatial distribution are still hardly available. Available digital topographic data sets created by National Mapping Agencies or mapped voluntarily through a crowd via Volunteered Geographic Information (VGI) platforms (e.g. OpenStreetMap) contain building footprint information but often lack additional information on building type, usage, age or number of

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Lawston, Patricia M., Joseph A. Santanello, Benjamin F. Zaitchik, and Matthew Rodell. "Impact of Irrigation Methods on Land Surface Model Spinup and Initialization of WRF Forecasts." Journal of Hydrometeorology 16, no. 3 (2015): 1135–54. http://dx.doi.org/10.1175/jhm-d-14-0203.1.

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Abstract In the United States, irrigation represents the largest consumptive use of freshwater and accounts for approximately one-third of total water usage. Irrigation impacts soil moisture and can ultimately influence clouds and precipitation through land–planetary boundary layer (PBL) coupling processes. This study utilizes NASA’s Land Information System (LIS) and the NASA Unified Weather Research and Forecasting Model (NU-WRF) framework to investigate the effects of drip, flood, and sprinkler irrigation methods on land–atmosphere interactions, including land–PBL coupling and feedbacks at t

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Manin, Iaroslav. "The legal regime of subsoil usage in the United States." Административное и муниципальное право, no. 1 (January 2021): 80–97. http://dx.doi.org/10.7256/2454-0595.2021.1.33753.

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The subject of this research is the legal regulation of exploitation of underground resources in the United States, while the object is the relations of subsoil usage. The author examines the system and structure of the federal executive branches that maintain the development of mineral deposits in the United States, including their functions and authority, highlighting the United States Department of the Interior and its regional branches. Special attention is given to constitutional framework of natural resource management, ownership rights to land and subsoil, its classification in causalit

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Gautam, Anita, and Bharath H. Aithal. "Multi-Sensor Satellite Data Analysis for Urban Land Use Pattern Recognition." IOP Conference Series: Earth and Environmental Science 1412, no. 1 (2024): 012032. https://doi.org/10.1088/1755-1315/1412/1/012032.

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Abstract A global phenomenon, urbanization is characterized by the expansion of cities and rapid growth of urban areas. As cities continue to grow, assessing the distribution and trends in land use is increasingly essential to identify, connect, quantify, validate, and predict the negative consequences of urban areas on the environment. In recent decades, the availability of multi-sensor data has revolutionized and emerged as a promising approach for improving efficiency and precision, and each sensor provides distinct capabilities as well as characteristics that make it possible for an in-dep

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Guizani, Douraied, Erika Buday-Bódi, János Tamás, and Attila Nagy. "Land Cover Modelling with Sentinel 2 in Water Balance Calculations of Urban Sites." Journal of Central European Green Innovation 11, no. 3 (2023): 70–83. http://dx.doi.org/10.33038/jcegi.4520.

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This study investigates how a land cover map is produced using Sentinel 2 satellite images and how the resulting map is integrated with the calculation of hydrological parameters. Due to its capacity to deliver high-resolution spatial data, satellite imagery is increasingly being used for land cover mapping (1). The process involves several stages, including pre-processing of the Sentinel 2 imagery, image classification using machine learning algorithms, and post-processing of the classified image to generate a land cover map. The resulting land cover map is then integrated with hydrological p

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Mahara, Arpan, and Naphtali Rishe. ""Integrating Location Information as Geohash Codes in Convolutional Neural Network-Based Satellite Image Classification"." IPSI Transactions on Internet Research 19, no. 02 (2023): 24–30. http://dx.doi.org/10.58245/ipsi.tir.2302.04.

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In the past few years, there have been many research studies conducted in the field of Satellite Image Classification. The purposes of these studies included flood identification, forest fire monitoring, greenery land identification, and land-usage identification. In this field, finding suitable data is often considered problematic, and some research has also been done to identify and extract suitable datasets for classification. Although satellite data can be challenging to deal with, Convolutional Neural Networks (CNNs), which consist of multiple interconnected neurons, have shown promising

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Aghababaei, Masoumeh, Ataollah Ebrahimi, Ali Asghar Naghipour, et al. "Introducing ARTMO’s Machine-Learning Classification Algorithms Toolbox: Application to Plant-Type Detection in a Semi-Steppe Iranian Landscape." Remote Sensing 14, no. 18 (2022): 4452. http://dx.doi.org/10.3390/rs14184452.

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Accurate plant-type (PT) detection forms an important basis for sustainable land management maintaining biodiversity and ecosystem services. In this sense, Sentinel-2 satellite images of the Copernicus program offer spatial, spectral, temporal, and radiometric characteristics with great potential for mapping and monitoring PTs. In addition, the selection of a best-performing algorithm needs to be considered for obtaining PT classification as accurate as possible . To date, no freely downloadable toolbox exists that brings the diversity of the latest supervised machine-learning classification a

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Hui, Ying, Yingkun Xie, Qing Yu, Xiaolei Liu, and Xinyuan Wang. "Hotspots Identification and Classification of Dockless Bicycle Sharing Service under Electric Fence Circumstances." Journal of Advanced Transportation 2022 (March 31, 2022): 1–16. http://dx.doi.org/10.1155/2022/5218254.

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Dockless bicycle sharing is one of the low-carbon transportation modes towards sustainable mobilities. Electric fences, as an effective solution for parking management, may have a high potential in guiding the usage of dockless bicycles at a low operation cost. However, new issues arise with the implementation of electric fences. The location of electric fences in hotspots fails to match the parking demand, leading the parking congestion in urban central areas. In this paper, a novel methodology of bicycle hotspots identification and classification is proposed to support parking management. An

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