A Convolutional Neural Network for Flood Mapping Using Sentinel-1 and SRTM Dem Data: Case Study in Poldokhtar-Iran

Abstract

Flood contributes a key role in devastating natural and man-made areas. Floods usually are occurred when there is a considerable number of clouds in the sky making optic data useless. Synthetic aperture radar (SAR) images can be a valuable data source in earth observation tasks. The most important characteristic of the radar image is its ability to penetrate the cloud and dust. Therefore, monitoring earth in cloudy or rainy weather can be available by this kind of dataset. In the last few years by improving machine learning methods and development of convolutional neural networks in remote sensing applications we are facing with extremely high improvement in classification tasks. In this paper, we use dual-polarized VV and VH backscatter values of Sentinel-1 and Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) dataset in a proposed convolutional neural network to generate a land cover map of a flooded area before and after happening. Obtained classification results vary between 93.3% to 98.5% for different training sizes. By comparing the generated classified maps, flooded areas of each class can be extracted.

Topo2vec: Topography Embedding Using the Fractal Effect

“…From ancient times, the topographic structure of land was a key aspect in many decisions. The topography of the land is provably correlated with many other tasks: land-use (Sheikh, Van Loon, and Stroosnijder 2014), soil mapping (Scull et al 2003), soil salinity (Divan and Adriaan 2017), landslides (Prakash, Manconi, and Loew 2020) water floods (Hosseiny, Ghasemian, and Amini 2019), avalanches (Jaedicke, Syre, and Sverdrup-Thygeson 2014) and high solar-energy locations (Heo et al 2020). The techniques for perceiving, collecting, and understanding topography has changed significantly in recent years and today, geographic information systems (GIS) are built on many classical and data-driven algorithms.…”