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Artykuły w czasopismach na temat „Land-based learning”

Autor: Grafiati

Data publikacji: 5 czerwca 2025

Data aktualizacji: 2 sierpnia 2025

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Bae-Dimitriadis, Michelle. "Land-Based Art Criticism: (Un)learning Land Through Art." Visual Arts Research 47, no. 2 (2021): 102–14. http://dx.doi.org/10.5406/visuartsrese.47.2.0102.

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Abstract This article provides an overview of how land-based settler colonial critique can reorient art criticism and art education to expand the scope of art and art practice to critical considerations of land politics and social justice, particularly in terms of the repatriation of Indigenous lands. In particular, land-based perspectives can help to rethink place/land by offering decolonizing methods for critiquing Western works of art that address place. Art educators’ ability to understand and critique settler colonialism in art has been hindered by Eurocentric art criticism. This article

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Ling, Feng, Yihang Zhang, Giles M. Foody, et al. "Learning-Based Superresolution Land Cover Mapping." IEEE Transactions on Geoscience and Remote Sensing 54, no. 7 (2016): 3794–810. http://dx.doi.org/10.1109/tgrs.2016.2527841.

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Spillett, Tasha. "Gender, Land, and Place: Considering Gender within Land-Based and Place-Based Learning." Journal for the Study of Religion, Nature and Culture 15, no. 1 (2021): 11–31. http://dx.doi.org/10.1558/jsrnc.39094.

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Rajeshyam Konka, Prakash. "Deep Learning for Land Use and Land Cover Classification Based on Optical Earth Observation Data: A Comprehensive Review." International Journal of Science and Research (IJSR) 13, no. 9 (2024): 1559–63. http://dx.doi.org/10.21275/sr24926074847.

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Pal, Mahesh. "Extreme‐learning‐machine‐based land cover classification." International Journal of Remote Sensing 30, no. 14 (2009): 3835–41. http://dx.doi.org/10.1080/01431160902788636.

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McKim, Aaron J., Abbey Palmer, Robert McKendree, Phillip Warsaw, and James DeDecker. "Evaluating land-based learning as a pedagogical approach." Journal of Agricultural Education 65, no. 3 (2024): 292–303. https://doi.org/10.5032/jae.v65i3.2767.

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Founded on the principles of place-based education, land-based learning collaboratively engages learners and community members in a four-step process of identification, understanding, intervention, and evaluation to enhance the sustainability of community-based agricultural systems. While scholars have provided the philosophical foundation for land-based learning, there have been no quantitative evaluations of learners engaged in this innovative pedagogical approach. Therefore, the current study explored students from two high schools in Michigan’s Upper Peninsula who participated in a land-ba

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Sanderson, Darlene, Noeman Mirza, and Heather Correale. "Indigenous Land-Based Experiential Learning in Nursing Education." Journal of Nursing Education 59, no. 12 (2020): 721. http://dx.doi.org/10.3928/01484834-20201118-12.

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Borrows, John. "OUTSIDER EDUCATION: INDIGENOUS LAW AND LAND-BASED LEARNING." Windsor Yearbook of Access to Justice 33, no. 1 (2017): 1. http://dx.doi.org/10.22329/wyaj.v33i1.4807.

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This article examines pedagogical developments in Canadian law schools related to outdoor education. In the process, it shows how recommendations from the Indian Residential Schools Truth and Reconciliation Commission can be applied, which called for law schools to create Indigenous-focused courses related to skills-based training in intercultural competency, conflict resolution, human rights and anti-racism. Land-based education on reserves can give law students meaningful context for exploring these Calls to Action. At the same time this article illustrates that taking students outside law s

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Ingram, Rebekah R., Ryan T. Ransom, and Kahente Horn-Miller. "O’nónna: A Curriculum for Land-Based Language Learning." Canadian Journal of Applied Linguistics 27, no. 2 (2024): 1–25. https://doi.org/10.37213/cjal.2024.34534.

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The Atlas of Kanyen’kehá:ka Space project (Kanyen’kehá:ka Nation, 2020; see www.mohawkatlas.org) launched in 2019 for the purposes of preserving Kanyen’kéha (Mohawk language) place names and related landscape terminology. Built using Nunaliit, a community mapping framework developed by Carleton University’s Geomatics and Cartographic Research Centre (GCRC), the Atlas is capable of pinning points onto a map and attaching media such as pronunciations, photos, videos and documents to that point. With funding for the Atlas through the Social Sciences and Humanities Research Council, the Atlas Rese

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Coiacetto, Eddo. "Learning from project based learning: a land development studio account of practice." Australian Planner 45, no. 4 (2008): 28–34. http://dx.doi.org/10.1080/07293682.2008.10753388.

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Abdullah, Azween, Daniel Arockiam, and Valliappan Raju. "Gradient based optimizer with deep learning based agricultural land use and land cover classification on SAR data." Journal of Infrastructure, Policy and Development 8, no. 8 (2024): 4488. http://dx.doi.org/10.24294/jipd.v8i8.4488.

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Agricultural land use and land cover (LULC) classification using synthetic aperture radar (SAR) data is a fundamental application in remote sensing and precision agriculture. Leveraging the abilities of SAR, which can enter over cloud cover and deliver detailed data about surface features, allows a robust analysis of agricultural landscapes. By harnessing the control of SAR data and innovative deep learning (DL) methods, this technique provides a complete solution for effectual and automatic agricultural land classification, paving the method for informed decision-making in present farming sys

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Zhang, Kai, Chengquan Hu, and Hang Yu. "Remote Sensing Image Land Classification Based on Deep Learning." Scientific Programming 2021 (December 24, 2021): 1–12. http://dx.doi.org/10.1155/2021/6203444.

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Aiming at the problems of high-resolution remote sensing images with many features and low classification accuracy using a single feature description, a remote sensing image land classification model based on deep learning from the perspective of ecological resource utilization is proposed. Firstly, the remote sensing image obtained by Gaofen-1 satellite is preprocessed, including multispectral data and panchromatic data. Then, the color, texture, shape, and local features are extracted from the image data, and the feature-level image fusion method is used to associate these features to realiz

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Rahmati, Omid, Fatemeh Falah, Seyed Amir Naghibi, et al. "Land subsidence modelling using tree-based machine learning algorithms." Science of The Total Environment 672 (July 2019): 239–52. http://dx.doi.org/10.1016/j.scitotenv.2019.03.496.

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Kong, In-Hak, Dong-Hoon Jeong, and Gu-Ha Jeong. "Development of Deep Learning-based Land Monitoring Web Service." Journal of Society of Korea Industrial and Systems Engineering 46, no. 3 (2023): 275–84. http://dx.doi.org/10.11627/jksie.2023.46.3.275.

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Heidari, Pooya, Asghar Milan, and Alireza Gharagozlou. "Land Cover and Land Use Extraction Based on Deep Learning Methods Using Satellite Images." Journal of Geomatics Science and Technology 14, no. 2 (2024): 119–33. https://doi.org/10.61186/jgst.14.2.119.

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Ashish V. Nimavat. "A NOVEL TRANSFER LEARNING BASED DEEP MODEL FOR LAND CLASSIFICATION." Journal of Electrical Systems 20, no. 3 (2024): 2089–96. http://dx.doi.org/10.52783/jes.4008.

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A systematic framework for comprehending the qualities and possibilities of different land portions is provided by land classification, which makes it easier to make well-informed decisions and implement sustainable land management techniques across a range of industries. LULC has numerous significant uses in a variety of fields such as Urban planning, agriculture, natural resource management, environmental assessment, infrastructure development, disaster management, Tourism and recreation, Transportation planning, water resource management, etc. Contemporary trends show deep learning technolo

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Rajesh, S., T. Gladima Nisia, S. Arivazhagan, and R. Abisekaraj. "Land Cover/Land Use Mapping of LISS IV Imagery Using Object-Based Convolutional Neural Network with Deep Features." Journal of the Indian Society of Remote Sensing 48, no. 1 (2019): 145–54. http://dx.doi.org/10.1007/s12524-019-01064-9.

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Abstract The paper proposes a new method for classifying the LISS IV satellite images using deep learning method. Deep learning method is to automatically extract many features without any human intervention. The classification accuracy through deep learning is still improved by including object-based segmentation. The object-based deep feature learning method using CNN is used to accurately classify the remotely sensed images. The method is designed with the technique of extracting the deep features and using it for object-based classification. The proposed system extracts deep features using

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Aggarwal, Kajal. "Machine Learning-Based Soil Classification." Mathematical Statistician and Engineering Applications 70, no. 1 (2021): 340–47. http://dx.doi.org/10.17762/msea.v70i1.2316.

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A crucial component of agriculture is soil. There are several varieties of dirt. Different properties may be found in each kind of soil, and various crops can be grown on various types of soils. To understand which crops do better in different soil types, we need to be aware of their features and traits. In this situation, machine learning approaches may be useful. It has made significant development in recent years. In the realm of agricultural data analysis, machine learning is still a young and difficult study area. In this study, we provide a model that predicts soil series with regard to

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Kussul, Nataliya N., Nikolay S. Lavreniuk, Andrey Yu Shelestov, Bogdan Ya Yailymov, and Igor N. Butko. "Land Cover Changes Analysis Based on Deep Machine Learning Technique." Journal of Automation and Information Sciences 48, no. 5 (2016): 42–54. http://dx.doi.org/10.1615/jautomatinfscien.v48.i5.40.

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Sun, Lijiao, Meng Xi, Zhengjian Li, Ziqiang Huo, Jiabao Wen, and Jiachen Yang. "Geospatial indexing for sea–land navigation based on machine learning." Computers and Electrical Engineering 118 (September 2024): 109433. http://dx.doi.org/10.1016/j.compeleceng.2024.109433.

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Rodrigues, Thanan, Frederico Takahashi, Arthur Dias, Taline Lima, and Enner Alcântara. "Machine Learning-Based Cerrado Land Cover Classification Using PlanetScope Imagery." Remote Sensing 17, no. 3 (2025): 480. https://doi.org/10.3390/rs17030480.

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The Cerrado domain, one of the richest on Earth, is among the most threatened in South America due to human activities, resulting in biodiversity loss, altered fire dynamics, water pollution, and other environmental impacts. Monitoring this domain is crucial for preserving its biodiversity and ecosystem services. This study aimed to apply machine learning techniques to classify the main vegetation formations of the Cerrado within the IBGE Ecological Reserve, a protected area in Brazil, using high-resolution PlanetScope imagery from 2021 to 2024. Three machine learning methods were evaluated: R

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Delgado, Tellez Ricardo, Wang Shaohua, Zhong Ershun, Cai Wenwen, and Long Liang. "Competitive Learning Approach to GIS Based Land Use Suitability Analysis." Journal of Resources and Ecology 7, no. 6 (2016): 430–37. http://dx.doi.org/10.5814/j.issn.1674-764x.2016.06.003.

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Hosseinzadeh, Elham, Sara Anamaghi, Massoud Behboudian, and Zahra Kalantari. "Evaluating Machine Learning-Based Approaches in Land Subsidence Susceptibility Mapping." Land 13, no. 3 (2024): 322. http://dx.doi.org/10.3390/land13030322.

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Land subsidence (LS) due to natural and human-driven forces (e.g., earthquakes and overexploitation of groundwater) has detrimental and irreversible impacts on the environmental, economic, and social aspects of human life. Thus, LS hazard mapping, monitoring, and prediction are important for scientists and decision-makers. This study evaluated the performance of seven machine learning approaches (MLAs), comprising six classification approaches and one regression approach, namely (1) classification and regression trees (CARTs), (2) boosted regression tree (BRT), (3) Bayesian linear regression (

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Kim, Jungsun, Jaewoong Won, Hyeongsoon Kim, and Joonghyeok Heo. "Machine-Learning-Based Prediction of Land Prices in Seoul, South Korea." Sustainability 13, no. 23 (2021): 13088. http://dx.doi.org/10.3390/su132313088.

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The accurate estimation of real estate value helps the development of real estate policies that can respond to the complexities and instability of the real estate market. Previously, statistical methods were used to estimate real estate value, but machine learning methods have gained popularity because their predictions are more accurate. In contrast to existing studies that use various machine learning methods to estimate the transactions or list prices of real estate properties without separating the building and land prices, this study estimates land price using a large amount of land-use i

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Kim, Yeseul, No-Wook Park, and Kyung-Do Lee. "Self-Learning Based Land-Cover Classification Using Sequential Class Patterns from Past Land-Cover Maps." Remote Sensing 9, no. 9 (2017): 921. http://dx.doi.org/10.3390/rs9090921.

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Hediyalad, Gangamma, K. Ashoka, Govardhan Hegade, Pratibha Ganapati Gaonkar, Azizkhan F. Pathan, and Pratibhaa R. Malagatti. "A comprehensive survey exploring the application of machine learning algorithms in the detection of land degradation." Journal of Degraded and Mining Lands Management 11, no. 4 (2024): 6471–88. http://dx.doi.org/10.15243/jdmlm.2024.114.6471.

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Early and reliable detection of land degradation helps policymakers to take strict action in more vulnerable areas by making strong rules and regulations in order to achieve sustainable land management and conservation. The detection of land degradation is carried out to identify desertification processes using machine learning techniques in different geographical locations, which are always a challenging issue in the global field. Due to the significance of the detection of land degradation, this article provides an exhaustive review of the detection of land degradation using machine learning

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Tariku, Girma, Isabella Ghiglieno, Andres Sanchez Morchio, et al. "Deep-Learning-Based Land Cover Mapping in Franciacorta Wine Growing Area." Applied Sciences 15, no. 2 (2025): 871. https://doi.org/10.3390/app15020871.

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Land cover mapping is essential to understanding global land-use patterns and studying biodiversity composition and the functioning of eco-systems. The introduction of remote sensing technologies and artificial intelligence models made it possible to base land cover mapping on satellite imagery in order to monitor changes, assess ecosystem health, support conservation efforts, and reduce monitoring time. However, significant challenges remain in managing large, complex satellite imagery datasets, acquiring specialized datasets due to high costs and labor intensity, including a lack of comparat

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Kalita, Indrajit, Runku Nikhil Sai Kumar, and Moumita Roy. "Deep Learning-Based Cross-Sensor Domain Adaptation Under Active Learning for Land Cover Classification." IEEE Geoscience and Remote Sensing Letters 19 (2022): 1–5. http://dx.doi.org/10.1109/lgrs.2021.3130285.

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Kavhu, Blessing, Zama Eric Mashimbye, and Linda Luvuno. "Climate-Based Regionalization and Inclusion of Spectral Indices for Enhancing Transboundary Land-Use/Cover Classification Using Deep Learning and Machine Learning." Remote Sensing 13, no. 24 (2021): 5054. http://dx.doi.org/10.3390/rs13245054.

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Accurate land use and cover data are essential for effective land-use planning, hydrological modeling, and policy development. Since the Okavango Delta is a transboundary Ramsar site, managing natural resources within the Okavango Basin is undoubtedly a complex issue. It is often difficult to accurately map land use and cover using remote sensing in heterogeneous landscapes. This study investigates the combined value of climate-based regionalization and integration of spectral bands with spectral indices to enhance the accuracy of multi-temporal land use/cover classification using deep learnin

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Jeong, Bongseok, Sunmin Lee, and Moung-jin Lee. "Classification of Subdivision Land Use and Land Cover Using Deep Learning Models." GEO DATA 6, no. 4 (2024): 535–51. https://doi.org/10.22761/gd.2024.0059.

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Land cover provides crucial information related to biological geography, ecological climatology, and human activities. In the past, land cover mapping was performed based on visual interpretation, but it had limitations in terms of time and cost. Recently, it has become possible to create land cover maps with higher temporal resolution over wider areas using artificial intelligence-based models. The accuracy and reliability of AI model-based land cover maps increase with the amount of training data, but it is difficult to acquire large amounts of data due to the time required for label data an

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Li, Chengqiang, Junxiao Wang, Liang Ge, Yujie Zhou, and Shenglu Zhou. "Optimization of Sample Construction Based on NDVI for Cultivated Land Quality Prediction." International Journal of Environmental Research and Public Health 19, no. 13 (2022): 7781. http://dx.doi.org/10.3390/ijerph19137781.

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The integrated use of remote sensing technology and machine learning models to evaluate cultivated land quality (CLQ) quickly and efficiently is vital for protecting these lands. The effectiveness of machine-learning methods can be profoundly influenced by training samples. However, in the existing research, samples have mainly been constructed by random point (RPO). Little attention has been devoted to the optimization of sample construction, which may affect the accuracy of evaluation results. In this study, we present two optimization methods for sample construction of random patch (RPA) an

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Vali, Ava, Sara Comai, and Matteo Matteucci. "Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review." Remote Sensing 12, no. 15 (2020): 2495. http://dx.doi.org/10.3390/rs12152495.

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Lately, with deep learning outpacing the other machine learning techniques in classifying images, we have witnessed a growing interest of the remote sensing community in employing these techniques for the land use and land cover classification based on multispectral and hyperspectral images; the number of related publications almost doubling each year since 2015 is an attest to that. The advances in remote sensing technologies, hence the fast-growing volume of timely data available at the global scale, offer new opportunities for a variety of applications. Deep learning being significantly suc

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Surana, Priya, Bhagwan Phulpagar, and Pramod Patil. "Fastai and Convolutional Neural Network Based Land Cover Classification." E3S Web of Conferences 405 (2023): 04044. http://dx.doi.org/10.1051/e3sconf/202340504044.

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The primary objective of this research is to create a Deep Learning model that can accurately classify satellite images into predefined categories. To accomplish this goal, we developed an effective approach for satellite image classification that utilizes deep learning and the convolutional neural network (CNN) for feature extraction. We trained our model using a labeled dataset of satellite images provided by Planet Labs, which specializes in detecting various types of land covers. By utilizing the CNN algorithm, we were able to automatically extract features from satellite data with relativ

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Sertel, Elif, Burak Ekim, Paria Ettehadi Osgouei, and M. Erdem Kabadayi. "Land Use and Land Cover Mapping Using Deep Learning Based Segmentation Approaches and VHR Worldview-3 Images." Remote Sensing 14, no. 18 (2022): 4558. http://dx.doi.org/10.3390/rs14184558.

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Deep learning-based segmentation of very high-resolution (VHR) satellite images is a significant task providing valuable information for various geospatial applications, specifically for land use/land cover (LULC) mapping. The segmentation task becomes more challenging with the increasing number and complexity of LULC classes. In this research, we generated a new benchmark dataset from VHR Worldview-3 images for twelve distinct LULC classes of two different geographical locations. We evaluated the performance of different segmentation architectures and encoders to find the best design to creat

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Vali, Ava, Sara Comai, and Matteo Matteucci. "An Automated Machine Learning Framework for Adaptive and Optimized Hyperspectral-Based Land Cover and Land-Use Segmentation." Remote Sensing 16, no. 14 (2024): 2561. http://dx.doi.org/10.3390/rs16142561.

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Hyperspectral imaging holds significant promise in remote sensing applications, particularly for land cover and land-use classification, thanks to its ability to capture rich spectral information. However, leveraging hyperspectral data for accurate segmentation poses critical challenges, including the curse of dimensionality and the scarcity of ground truth data, that hinder the accuracy and efficiency of machine learning approaches. This paper presents a holistic approach for adaptive optimized hyperspectral-based land cover and land-use segmentation using automated machine learning (AutoML).

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Irfan, Ayesha, Yu Li, Xinhua E, and Guangmin Sun. "Land Use and Land Cover Classification with Deep Learning-Based Fusion of SAR and Optical Data." Remote Sensing 17, no. 7 (2025): 1298. https://doi.org/10.3390/rs17071298.

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Land use and land cover (LULC) classification through remote sensing imagery serves as a cornerstone for environmental monitoring, resource management, and evidence-based urban planning. While Synthetic Aperture Radar (SAR) and optical sensors individually capture distinct aspects of Earth’s surface, their complementary nature SAR excelling in structural and all-weather observation and optical sensors providing rich spectral information—offers untapped potential for improving classification robustness. However, the intrinsic differences in their imaging mechanisms (e.g., SAR’s coherent scatter

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Datta, Ranjan, Rajmoni Singha, and Margot Hurlbert. "Indigenous Land-Based Perspectives on Environmental Sustainability: Learning from the Khasis Indigenous Community in Bangladesh." Sustainability 16, no. 9 (2024): 3678. http://dx.doi.org/10.3390/su16093678.

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This research explores Indigenous land-based perspectives on environmental sustainability, centering on the Khasis Indigenous community in Bangladesh. With a critical connection to their land-based cultural heritage and environment, the Khasis community offers a distinctive perspective for examining environmental challenges. Emphasizing the traditional land-based knowledge and practices of the Khasis, as well as their insights on environmental challenges, this study employs a land-based theoretical framework. It sheds light on the adaptive strategies of Khasis Indigenous communities amidst shi

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Alshari, Eman A., and Bharti W. Gawali. "Analysis of Machine Learning Techniques for Sentinel-2A Satellite Images." Journal of Electrical and Computer Engineering 2022 (May 16, 2022): 1–16. http://dx.doi.org/10.1155/2022/9092299.

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This article presents the comparative analysis of classification techniques to assign land use and land cover classes from different strategies (pixel-based, object-based, rule-based, distance-based, and neural-based) with a Sentinel-2A satellite image for 2016. The study area is the Sana’a city of Yemen which covers about 18,796.88 km2 land area. This research aims to present the fundamentals of supervised machine learning approaches, including their limitations and strengths and experimentation for twelve classifiers. The outcome of experimentation showed that the Random Forest could be a go

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Rimba, Andi Besse, Andi Arumansawang, I. Putu Wira Utama, et al. "Cloud-Based Machine Learning for Flood Policy Recommendations in Makassar City, Indonesia." Water 15, no. 21 (2023): 3783. http://dx.doi.org/10.3390/w15213783.

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Makassar City frequently experiences monsoonal floods, typical of a tropical city in Indonesia. However, there is no high-accuracy flood map for flood inundation. Examining the flood inundation area would help to provide a suitable flood policy. Hence, the study utilizes multiple satellite data sources on a cloud-based platform, integrating the physical factors of a flood (i.e., land use data and digital elevation model—DEM—data) with the local government’s urban land use plan and existing drainage networks. The research aims to map the inundation area, identify the most vulnerable land cover,

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Gao, Lianru, Yiqun He, Xu Sun, Xiuping Jia, and Bing Zhang. "Incorporating Negative Sample Training for Ship Detection Based on Deep Learning." Sensors 19, no. 3 (2019): 684. http://dx.doi.org/10.3390/s19030684.

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While ship detection using high-resolution optical satellite images plays an important role in various civilian fields—including maritime traffic survey and maritime rescue—it is a difficult task due to influences of the complex background, especially when ships are near to land. In current literatures, land masking is generally required before ship detection to avoid many false alarms on land. However, sea–land segmentation not only has the risk of segmentation errors, but also requires expertise to adjust parameters. In this study, Faster Region-based Convolutional Neural Network (Faster R-C

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Aristin, Nevy Farista, Budijanto Budijanto, Didik Taryana, and I. Nyoman Ruja. "3D Map of Dry Land Use Based Aerial Image as Learning Media in Era of Education 4.0." International Journal of Emerging Technologies in Learning (iJET) 15, no. 07 (2020): 171. http://dx.doi.org/10.3991/ijet.v15i07.13327.

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Now, this is the era of the industrial revolution 4.0, where technological innovation is developing rapidly so that education also demands to change into the era of education 4.0 immediately. The era of education 4.0, was characterized by the use of technology in the learning process. However, in reality, the learning process still facing many obstacles, one of which in terms of the use of instructional media. Learning media still use conventional and limited to Microsoft PowerPoint software which only contains writings for the presentation of dry land use material. This makes it difficult for

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Zhao, Shengyu, Kaiwen Tu, Shutong Ye, Hao Tang, Yaocong Hu, and Chao Xie. "Land Use and Land Cover Classification Meets Deep Learning: A Review." Sensors 23, no. 21 (2023): 8966. http://dx.doi.org/10.3390/s23218966.

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As one of the important components of Earth observation technology, land use and land cover (LULC) image classification plays an essential role. It uses remote sensing techniques to classify specific categories of ground cover as a means of analyzing and understanding the natural attributes of the Earth’s surface and the state of land use. It provides important information for applications in environmental protection, urban planning, and land resource management. However, remote sensing images are usually high-dimensional data and have limited available labeled samples, so performing the LULC

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De Finney, Sandrina, Sarah Wright Cardinal, Morgan Mowatt та ін. "ȻENTOL TŦE TEṈEW̱ (TOGETHER WITH THE LAND)". International Journal of Child, Youth and Family Studies 11, № 3 (2020): 34–55. http://dx.doi.org/10.18357/ijcyfs113202019698.

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In this paper, Part 2 of a two-paper series, we extend our learning on land- and water-based pedagogies from Part 1 to outline broader debates about upholding resurgence in frontline practice with Indigenous children, youth, and families. This article shares key learning from an Indigenous land- and water-based institute held from 2019 to 2020 that was facilitated by knowledge keepers from local First Nations and coordinated by faculty mentors from the School of Child and Youth Care at the University of Victoria. The purpose of the one-year institute was to convene a circle of Indigenous gradu

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Razafinimaro, Arisetra, Aimé Richard Hajalalaina, Hasina Rakotonirainy, and Reziky Zafimarina. "Land cover classification based optical satellite images using machine learning algorithms." International Journal of Advances in Intelligent Informatics 8, no. 3 (2022): 362. http://dx.doi.org/10.26555/ijain.v8i3.803.

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This article aims to apply machine learning algorithms to the supervised classification of optical satellite images. Indeed, the latter is efficient in the study of land use. Despite the performance of machine learning in satellite image processing, this can change but depends on the nature of the satellite images used. Moreover, when we use the satellite, then the reliability of one classifier can be different from the others. In this paper, we examined the performance of DT, SVM, KNN, ANN, and RF. Analysis factors were used to investigate further their importance for Sentinel 2, Landsat 8, T

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Alali, Dhufr Hussein, and Timur Inan. "A Decision Support System Based on Machine Learning for Land Investment." Journal of Education and Science 32, no. 4 (2023): 34–47. http://dx.doi.org/10.33899/edusj.2023.141005.1375.

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Jamali, Ali. "Land use land cover mapping using advanced machine learning classifiers." Ekológia (Bratislava) 40, no. 3 (2021): 286–300. http://dx.doi.org/10.2478/eko-2021-0031.

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Abstract Due to the recent climate changes such as floods and droughts, there is a need for Land Use Land Cover (LULC) mapping to monitor environmental changes that have effects on ecology, policy management, health and disaster management. As such, in this study, two well-known machine learning classifiers, namely, Support Vector Machine (SVM) and Random Forest (RF), are used for land cover mapping. In addition, two advanced deep learning algorithms, namely, the GAMLP and FSMLP, that are based on the Multi-layer Perceptron (MLP) function are developed in MATLAB programming language. The GAMLP

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Wang, Yue, Wanshun Zhang, Xin Liu, et al. "A Deep Learning Method for Land Use Classification Based on Feature Augmentation." Remote Sensing 17, no. 8 (2025): 1398. https://doi.org/10.3390/rs17081398.

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Land use monitoring by satellite remote sensing can improve the capacity of ecosystem resources management. The satellite source, bandwidth, computing speed, data storage and cost constrain the development and application in the field. A novel deep learning classification method based on feature augmentation (CNNs-FA) is developed in this paper, which offers a robust avenue to realize regional low-cost and high-precision land use monitoring. Twenty-two spectral indices are integrated to augment vegetation, soil and water features, which are used for convolutional neural networks (CNNs) learnin

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Møller, Anders Bjørn, Vera Leatitia Mulder, Gerard B. M. Heuvelink, Niels Mark Jacobsen, and Mogens Humlekrog Greve. "Can We Use Machine Learning for Agricultural Land Suitability Assessment?" Agronomy 11, no. 4 (2021): 703. http://dx.doi.org/10.3390/agronomy11040703.

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It is vital for farmers to know if their land is suitable for the crops that they plan to grow. An increasing number of studies have used machine learning models based on land use data as an efficient means for mapping land suitability. This approach relies on the assumption that farmers grow their crops in the best-suited areas, but no studies have systematically tested this assumption. We aimed to test the assumption for specialty crops in Denmark. First, we mapped suitability for 41 specialty crops using machine learning. Then, we compared the predicted land suitabilities with the mechanist

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Westi, Utami, Sugiyanto Catur, and Rahardjo Noorhadi. "Artificial intelligence in land use prediction modeling: a review." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 2514–23. https://doi.org/10.11591/ijai.v13.i3.pp2514-2523.

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This study aims to review methods of artificial intelligence (AI) in land use modelling. Data were extracted from journals in the Scopus and Google Scholar databases using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) method. The review demonstrates that modelling land use predictions is a complex matter that involves land use maps and driving forces. AI technology can support land use forecasting by interpreting land use data, analyzing drivers, and modeling. However, AI has limitations in terms of broad contextual understanding and algorithmic errors. To ant

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Hu, Wenyi, Xiaomeng Jiang, Jiawei Tian, Shitong Ye, and Shan Liu. "Land Target Detection Algorithm in Remote Sensing Images Based on Deep Learning." Land 14, no. 5 (2025): 1047. https://doi.org/10.3390/land14051047.

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Remote sensing technology plays a crucial role across various sectors, such as meteorological monitoring, city planning, and natural resource exploration. A critical aspect of remote sensing image analysis is land target detection, which involves identifying and classifying land-based objects within satellite or aerial imagery. However, despite advancements in both traditional detection methods and deep-learning-based approaches, detecting land targets remains challenging, especially when dealing with small and rotated objects that are difficult to distinguish. To address these challenges, thi

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