Thematische Bibliographien / Land usage classification

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Auswahl der wissenschaftlichen Literatur zum Thema „Land usage classification“

Autor: Grafiati

Veröffentlicht am 7. Juni 2025

Zuletzt aktualisiert am 2. August 2025

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Zeitschriftenartikel zum Thema "Land usage classification"

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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Dissertationen zum Thema "Land usage classification"

Burck, Michael Theodore. "MAPPING RIPARIAN BUFFER ZONES IN CYPRESS CREEK REFUGE, ILLINOIS: LAND USE CHANGE IMPACT ON HABITAT USAGE FROM 1984-2014: PASSERINE PRESENCE AND CLASSIFICATION COMPARISONS." OpenSIUC, 2017. https://opensiuc.lib.siu.edu/theses/2229.

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In response to recent declines, forested riparian wetland areas have become an increased conservation and management area of concern focusing on increasing biodiversity and promoting healthy ecosystem services. Additionally, passerine birds have also experienced a sharp global decline in that associated habitat. To mitigate further declines of both habitat and species numbers government programs and agencies have intensified conservation efforts. However, the practices employed are often assumed to be beneficial without conducting dedicated surveys to measure efficacy and practicality of cu

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Bayoudh, Meriam. "Apprentissage de connaissances structurelles à partir d’images satellitaires et de données exogènes pour la cartographie dynamique de l’environnement amazonien." Thesis, Antilles-Guyane, 2013. http://www.theses.fr/2013AGUY0671/document.

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Les méthodes classiques d'analyse d'images satellites sont inadaptées au volume actuel du flux de données. L'automatisation de l'interprétation de ces images devient donc cruciale pour l'analyse et la gestion des phénomènes observables par satellite et évoluant dans le temps et l'espace. Ce travail vise à automatiser la cartographie dynamique de l'occupation du sol à partir d'images satellites, par des mécanismes expressifs, facilement interprétables en prenant en compte les aspects structurels de l'information géographique. Il s'inscrit dans le cadre de l'analyse d'images basée objet. Ainsi,

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Bücher zum Thema "Land usage classification"

Wich, Serge A., and Lian Pin Koh. Conservation Drones. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198787617.001.0001.

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In this book, we introduce the use of drones for wildlife conservation. We provide a broad overview of when drone technology can be useful for wildlife conservation before going into the different types of drones that are available and the basic configuration of such systems. After this we discuss the various types of sensors that are being used to obtain data and the various applications for those sensors by us and others. We discuss the various applications of sensors and discuss research that we and others have conducted with those. The usage of drones for surveillance is discussed as well

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Buchteile zum Thema "Land usage classification"

Romanciuc, Inna, Lesia Yelistratova, Alexandr Apostolov, and Victor Chekhniy. "Remote Sensing Methodology to Study Wetlands Under Conditions of Climate Change." In Handbook of Research on Water Sciences and Society. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-7356-3.ch024.

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Wetlands, marshes, and swamps have great natural importance. They have a number of important environmental functions, supporting the water balance of the area and ensuring its high biodiversity. This is reflected in the wide range of ecosystem services provided within this area. At the same time, they are one of the most vulnerable ecosystems on the planet. The study of the carbon cycle, emissions, and absorption in the current climate change and conditions and anthropogenic pressures are important and urgent tasks in terms of refining the climate models and more accurate determination of clim

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Lohmann Peter, Soergel Uwe, and Farghaly Dalia. "Classification of Agricultural Sites Using Time-Series of High-Resolution Dual-Polarisation TerraSAR – X Spotlight Images." In Imagin[e,g] Europe. IOS Press, 2010. https://doi.org/10.3233/978-1-60750-494-8-249.

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Increasing demands for lasting and environmentally conscious use of natural resources together with a cost effective and restrictive use of fertilizers and pesticides require the employment of new technologies in agriculture. The preliminary results presented here consist in the automatic land use classification of agricultural fields based on multi-temporal TerraSAR-X images in dual polarization obtained in the high resolution Spotlight mode of the satellite. The classified data in turn can be used to enhance and validate existing models on ground water quality as a function of agricultural u

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Konferenzberichte zum Thema "Land usage classification"

M.R., Vijay Krishnan, Raghav Mehra, and Hari Prasada Raju Kunadharaju. "Design of Neural Network based Approaches for Land Usage Land Cover Classification." In 2024 Third International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT). IEEE, 2024. http://dx.doi.org/10.1109/iceeict61591.2024.10718442.

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Mohammadyari, Fatemeh, Mir Mehrdad Mirsanjari, Jūratė Sužiedelytė Visockienė, and Ardavan Zarandian. "Evaluation of Change in Land Usage and Land Cover in Karaj, Iran." In 11th International Conference “Environmental Engineering”. VGTU Technika, 2020. http://dx.doi.org/10.3846/enviro.2020.649.

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In this study, classification results were derived from remote sensing data and the Support Vector Machine (SVM) algorithm used in this process, which classifies Landsat land-cover images. The accuracy of image classifications was evaluated by calculation of the Kappa coefficient. The area of study is Karaj, the capital of Alborz province, in north-central Iran. It is situated in the foothills of the Alborz Mountains and occupies a fertile agricultural plain. Landsat data used in the classification of land cover were collected from USGS websites, and multi-temporal images from the data were ge

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Wang, Ke, Craig Stutts, Enrique Dunn, and Jan-Michael Frahm. "Efficient joint stereo estimation and land usage classification for multiview satellite data." In 2016 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2016. http://dx.doi.org/10.1109/wacv.2016.7477657.

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Rao, Duvvada Rajeswara, Shaik Noorjahan, and Shaik Ayesha Fathima. "Classification of Land Cover Usage from Satellite Images using Deep Learning Algorithms." In 2022 International Conference on Electronics and Renewable Systems (ICEARS). IEEE, 2022. http://dx.doi.org/10.1109/icears53579.2022.9752282.

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Purwar, Prateek, Savvas Rogotis, Fotis Chatzipapadopoulus, and Iason Kastanis. "A Reliable Approach for Pixel-Level Classification of Land usage from Spatio-Temporal Images." In 2019 6th Swiss Conference on Data Science (SDS). IEEE, 2019. http://dx.doi.org/10.1109/sds.2019.00004.

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Zhang, Yang, Ruohan Zong, Jun Han, et al. "TransLand: An Adversarial Transfer Learning Approach for Migratable Urban Land Usage Classification using Remote Sensing." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006360.

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Grigoriev, Mikhail M., Chester V. Swiatek, and James A. Hitt. "Benchmarking CD-Adapco’s Star-CCM+ in a Production Design Environment." In ASME Turbo Expo 2010: Power for Land, Sea, and Air. ASMEDC, 2010. http://dx.doi.org/10.1115/gt2010-23627.

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It has been well-known that many industrial applications that use centrifugal compressors have continuously demanded for more and more efficient machines in the past decade or so. However, the current market trend indicates that there is a strong demand among the end users for reduced delivery times of these efficient compressors. This, in turn, requires that the aerodynamic design of highly efficient centrifugal stages be completed within shorter time frames. In order to meet these challenges, it is mandatory to involve modern computational analysis tools such as Computational Fluid Dynamics

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Zhang, Mingyang, Tong Li, Yong Li, and Pan Hui. "Multi-View Joint Graph Representation Learning for Urban Region Embedding." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/611.

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The increasing amount of urban data enable us to investigate urban dynamics, assist urban planning, and eventually, make our cities more livable and sustainable. In this paper, we focus on learning an embedding space from urban data for urban regions. For the first time, we propose a multi-view joint learning model to learn comprehensive and representative urban region embeddings. We first model different types of region correlations based on both human mobility and inherent region properties. Then, we apply a graph attention mechanism in learning region representations from each view of the b

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Berichte der Organisationen zum Thema "Land usage classification"

Lasko, Kristofer, and Sean Griffin. Monitoring Ecological Restoration with Imagery Tools (MERIT) : Python-based decision support tools integrated into ArcGIS for satellite and UAS image processing, analysis, and classification. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40262.

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Monitoring the impacts of ecosystem restoration strategies requires both short-term and long-term land surface monitoring. The combined use of unmanned aerial systems (UAS) and satellite imagery enable effective landscape and natural resource management. However, processing, analyzing, and creating derivative imagery products can be time consuming, manually intensive, and cost prohibitive. In order to provide fast, accurate, and standardized UAS and satellite imagery processing, we have developed a suite of easy-to-use tools integrated into the graphical user interface (GUI) of ArcMap and ArcG

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Ruiz, Pablo, Craig Perry, Alejando Garcia, et al. The Everglades National Park and Big Cypress National Preserve vegetation mapping project: Interim report—Northwest Coastal Everglades (Region 4), Everglades National Park (revised with costs). National Park Service, 2020. http://dx.doi.org/10.36967/nrr-2279586.

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The Everglades National Park and Big Cypress National Preserve vegetation mapping project is part of the Comprehensive Everglades Restoration Plan (CERP). It is a cooperative effort between the South Florida Water Management District (SFWMD), the United States Army Corps of Engineers (USACE), and the National Park Service’s (NPS) Vegetation Mapping Inventory Program (VMI). The goal of this project is to produce a spatially and thematically accurate vegetation map of Everglades National Park and Big Cypress National Preserve prior to the completion of restoration efforts associated with CERP. T

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