Name
Abstract | ||
|---|---|---|
Detecting changes in impervious surface cover is one of the most important topics in land cover and land use (LCLU) change. This study focuses on detecting infrastructure constructions, such as residential areas, commercial building, and roads, in the State of Maryland (US) from 2018 to 2019 by utilizing Sentinel-2 images at 10 m spatial resolution. We use a time-series of Sentinel-2 images to derive land cover maps in 2018 and 2019 and derive the change detection map. The multi-layer perceptron (MLP) neural network is used to classify satellite images into general land cover classes (impervious surface, forest/tree cover, grassland/cropland, water). The derived change detection map allows one to identify areas of changes with new constructions. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/IGARSS39084.2020.9323327 | IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM |
Keywords | DocType | Citations |
Sentinel-2, impervious surface, change map, neural networks | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yiming Zhang | 1 | 0 | 0.34 |
| Sergii Skakun | 2 | 1 | 2.39 |
| Victor Prudente | 3 | 0 | 0.34 |