A deep classification model to assess environment following hazards using remote sensing images
Abstract Environmental hazards are materials, states, or situations that threaten the natural environment or human health, such as pollution and natural catastrophes like hurricanes and earthquakes. In recent decades, natural hazards have become more dangerous due to developments affecting climate a...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-07-01
|
| Series: | Discover Applied Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s42452-025-07445-9 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Abstract Environmental hazards are materials, states, or situations that threaten the natural environment or human health, such as pollution and natural catastrophes like hurricanes and earthquakes. In recent decades, natural hazards have become more dangerous due to developments affecting climate and land use/land cover (LULC), primarily driven by anthropic pressures such as urbanization, forest management methods, and agricultural activities. Advancements in Remote Sensing (RS) technology enable rapid, accurate terrain data collection, significantly aiding in mapping, monitoring, and assessing hazards. This research proposes a deep classification model combining hierarchical feature extraction and classification units to categorize LULC from remotely sensed images. Four filters of equal size (3 × 3) simultaneously extract features from the input image, which are then concatenated and classified into different LULC categories. Experiments on two datasets independently verify the model, demonstrating improved resilience compared to other state-of-the-art approaches. To ensure the generalizability and robustness of the model, 5-fold cross-validation is conducted, yielding consistently high AUC scores. Additionally, an independent T-test is performed to statistically validate the performance improvements over comparative models. This proposed model helps predict future impacts and manage risks through accurate and efficient LULC classification. |
|---|---|
| ISSN: | 3004-9261 |