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Title: Developments in land use and land cover classification techniques in remote sensing: a review.
Affiliation: LUCRÊNCIO SILVESTRE MACARRINGUE, UNICAMP, Instituto Politécnico de Ciências da Terra e Ambiente, Matola, Mozambique; EDSON LUIS BOLFE, CNPTIA, Unicamp; PAULO ROBERTO MENDES PEREIRA, UNICAMP.
Date Issued: 2022
Citation: Journal of Geographic Information System, v. 14, n. 1, p. 1-28, Feb. 2022.
Description: Abstract. Studies on land use and land cover changes (LULCC) have been a great concern due to their contribution to the policies formulation and strategic plans in different areas and at different scales. The LULCC when intense and on a global scale can be catastrophic if not detected and monitored affecting the key aspects of the ecosystem´s functions. For decades, technological developments and tools of geographic information systems (GIS), remote sensing (RS) and machine learning (ML) since data acquisition, processing and results in diffusion have been investigated to access landscape conditions and hence, different land use and land cover classification systems have been performed at different levels. Providing coherent guidelines, based on literature review, to examine, evaluate and spread such conditions could be a rich contribution. Therefore, hundreds of relevant studies available in different databases (Science Direct, Scopus, Google Scholar) demonstrating advances achieved in local, regional and global land cover classification products at different spatial, spectral and temporal resolutions over the past decades were selected and investigated. This article aims to show the main tools, data, approaches applied for analysis, assessment, mapping and monitoring of LULCC and to investigate some associated challenges and limitations that may influence the performance of future works, through a progressive perspective. Based on this study, despite the advances archived in recent decades, issues related to multi-source, multi-temporal and multi-level analysis, robustness and quality, scalability need to be further studied as they constitute some of the main challenges for remote sensing.
Thesagro: Sensoriamento Remoto
Uso da Terra
NAL Thesaurus: Remote sensing
Land use
Land cover
Keywords: Cobertura da terra
Dados espaciais
Computação em nuvem
Aprendizado de máquina
Big data
Big Spatial Data
Cloud Computing
Machine Learning
Language: Ingles
Type of Material: Artigo de periódico
Access: openAccess
Appears in Collections:Artigo em periódico indexado (CNPTIA)

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