| Title |
Semantic segmentation for change detection in satellite imaging / |
| Authors |
Kömürcü, Kürşat ; Petkevičius, Linas |
| DOI |
10.15388/LMITT.2024.8 |
| Full Text |
|
| Is Part of |
Lietuvos magistrantų informatikos ir IT tyrimai: konferencijos darbai, 2024 m. gegužės 10 d... Vilnius : Vilniaus universiteto leidykla. 2024, p. 57-64.. eISSN 2783-784X |
| Keywords [eng] |
Deep learning ; semantic segmentation ; change detection ; satellite imagery ; Vector Autoregression |
| Abstract [eng] |
Change detection is a common and actual problem in the field of remote sensing. The classical approaches using raw pixel information are very sensitive to noise. In this study we propose the usage of additional semantic information for change detection. We use the semantic segmentation methods like geospatial Segment Anything Model and encoder based U-Net to evaluate the predictions and tracing the semantic information as well as raw information in change detection. Later the multidimensional time series data is used via the Vector Autoregression model to predict the future changes in the landscape. The observations which fall out of the prediction interval are considered as the changes in the landscape. The proposed method is evaluated on the dataset of the random locations across the Baltic region. The research is accompanied by the data and reproducible code at Github repository. |
| Published |
Vilnius : Vilniaus universiteto leidykla |
| Type |
Conference paper |
| Language |
English |
| Publication date |
2024 |
| CC license |
|
