Urban areas analysis using satellite image segmentation and deep neural network

Abstract

The goal of our research was to develop methods based on convolutional neural networks for automatically extracting the locations of buildings from high-resolution aerial images. To analyze the quality of developed deep learning algorithms, there was used Sorensen-Dice coefficient of similarity which compares results of algorithms with real masks. These masks were generated automatically from json files and sliced on smaller parts together with respective aerial photos before the training of developed convolutional neural networks. This approach allows us to cope with the problem of segmentation for high-resolution satellite images. All in all we show how deep neural networks implemented and launched on modern GPUs of high-performance supercomputer NVIDIA DGX-1 can be used to efficiently learn and detect needed objects. The problem of building detection on satellite images can be put into practice for urban planning, building control of some municipal objects, search of the best locations for future outlets etc.

The Use of Deep Learning in Remote Sensing for Mapping Impervious Surface: A Review Paper

“…The average training accuracy of the UNet model was 0.83, while the testing accuracy was 0.87. (Khryaschev and Ivanovsky 2019)create a convolutional neural network-based approach for automatically extracting building locations from high-resolution aerial imagery The Sorensen-Dice coefficient of similarity was utilized by the researcher to compare the outcomes of algorithms with genuine masks. Before training the created convolutional neural networks, these masks were produced automatically and split into smaller portions in conjunction with corresponding aerial images.…”

“…The majority of the Deep learning studies that applied to impervious surfaces or built-up areas were split between semantic image segmentation, image classification object-based and hybrid method; object-based deep CNN -hybrid (Fu et al 2019),classification (Tan et al 2017), Semantic segmentation (Bodani et al 2017;Iqbal and Ali 2020;Irwansyah et al 2020;Khryaschev and Ivanovsky 2019;Khurshid and Khan 2013;Núñez 2015b;Zhao et al 2014).…”