Open AccessCLASSIFICAÇÃO DE SUPERFÍCIES IMPERMEÁVEIS EM IMAGEM MULTIESPECTRAL COM ALGORITMO DE MACHINE LEARNING
Author(s) -
Michelle Taís Garcia Furuya,
Danielle Elis Garcia Furuya,
Lucas Prado Osco,
Altina Ramos
Publication year - 2021
Publication title -
colloquium exactarum
Language(s) - English
Resource type - Journals
ISSN - 2178-8332
DOI - 10.5747/ce.2021.v13.n3.e368
Subject(s) - impervious surface , computer science , context (archaeology) , urbanization , support vector machine , land cover , process (computing) , scale (ratio) , remote sensing , artificial intelligence , urban planning , machine learning , land use , geography , cartography , civil engineering , engineering , ecology , archaeology , economics , biology , economic growth , operating system
The urbanization process exposes the urban landscape to rapid and constant transformations. The change in land use and land cover patterns directly impacts the quality of life in cities. Therefore, monitoring the urban territorial composition becomes essential for urban management. To gain access to these data, studies have been applying remote sensing techniques combined with machine learning. Satellite images provide large-scale data with high temporal resolution, making it easier to detect changes in the landscape. Machine learning algorithms, on the other hand, provide classifications with greater accuracy compared to traditional methods. From this context and the available techniques, the study aims to evaluate the performance of the Support Vector Machine (SVM) algorithm in quantifying impervious areas in the urban perimeter of Presidente Prudente from a Planet image. The classification process was done using ArcGIS Pro software. The results demonstrate high performance for the SVM when applied in classification of impervious areas in urban territory. The accuracy of 94% shows that the method proposed in the work is useful as a tool for urban planning.