DGAG: A Novel Approach That Detects Green Areas With Geolocation From Large‐Scale Satellite Imaging Instance Segmentation and GIS Application

Abstract

ABSTRACTAdvancements in space exploration and computing have accelerated progress in remote sensing studies, where imaging satellites capture extensive datasets globally, particularly in identifying green areas on agricultural lands essential for monitoring natural resources, promoting sustainable agriculture, and mitigating climate change. Large‐volume satellite images from 2020 were obtained from https://tile.kayseri.bel.tr/tilecache/Cache/2020UYDU3857/z/x/y.jpeg. The images are stored on the server address of Kayseri Metropolitan Municipality. Traditional techniques struggle with classifying big data from these satellite views, necessitating innovative approaches like DGAG (Detect Green Areas with Geolocation), a novel method that combines interdisciplinary techniques to detect and geographically delineate green areas on agricultural lands globally. DGAG utilizes map‐based open‐source software to convert large‐scale satellite views into processable images with spatial information, employing segmentation‐based deep learning techniques such as You Only Look Once version 5 (YOLOv5) and Mask Region‐based Convolutional Neural Network (Mask R‐CNN) to extract green areas and determine pixel boundaries. The pixel borders are then transformed into spatial polygon data, providing hectare‐level spatial information. Testing on actual satellite views of Kayseri province yielded promising results, with DGAG YOLOv5 and Mask R‐CNN achieving F1 scores of 0.917 and 0.922, respectively. Notably, DGAG Mask R‐CNN outperformed YOLOv5 by detecting 834626.42 square meters more green area. In terms of runtime, DGAG Mask R‐CNN detected green areas in approximately 0.031 s, while DGAG YOLOv5 operated roughly twice as fast, detecting green areas in about 0.015 s.