Deep Learning-Driven Multi-Temporal Detection: Leveraging DeeplabV3+/Efficientnet-B08 Semantic Segmentation for Deforestation and Forest Fire Detection
Deforestation and forest fires are escalating global threats that require timely, scalable, and cost-effective monitoring systems. While UAV and ground-based solutions offer fine-grained data, they are often constrained by limited spatial coverage, high operational costs, and logistical challenges....
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/14/2333 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849521428195442688 |
|---|---|
| author | Joe Soundararajan Andrew Kalukin Jordan Malof Dong Xu |
| author_facet | Joe Soundararajan Andrew Kalukin Jordan Malof Dong Xu |
| author_sort | Joe Soundararajan |
| collection | DOAJ |
| description | Deforestation and forest fires are escalating global threats that require timely, scalable, and cost-effective monitoring systems. While UAV and ground-based solutions offer fine-grained data, they are often constrained by limited spatial coverage, high operational costs, and logistical challenges. In contrast, satellite imagery provides broad, repeatable, and economically feasible coverage. This study presents a deep learning framework that combines the DeepLabV3+ architecture with an EfficientNet-B08 backbone to address both deforestation and wildfire detection using satellite imagery. The system utilizes advanced multi-scale feature extraction and Group Normalization to enable robust semantic segmentation under challenging atmospheric conditions and complex forest structures. It is evaluated on two benchmark datasets. In the Amazon forest segmentation dataset, the model achieves a validation Intersection over Union (IoU) of 0.9100 and a pixel accuracy of 0.9605, demonstrating strong performance in delineating forest boundaries. In FireDataset_20m, which presents a severe class imbalance between fire and non-fire pixels, the framework achieves 99.95% accuracy, 93.16% precision, and 91.47% recall. A qualitative analysis confirms the model’s ability to accurately localize fire hotspots and deforested areas. These results highlight the model’s dual-purpose utility for high-resolution, multi-temporal environmental monitoring. Its balanced performance across metrics and adaptability to complex terrain conditions make it a promising tool for supporting forest conservation, early fire detection, and evidence-based policy interventions. |
| format | Article |
| id | doaj-art-02e1a7d930b943dfae7067d02ba0b630 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-02e1a7d930b943dfae7067d02ba0b6302025-08-20T03:32:27ZengMDPI AGRemote Sensing2072-42922025-07-011714233310.3390/rs17142333Deep Learning-Driven Multi-Temporal Detection: Leveraging DeeplabV3+/Efficientnet-B08 Semantic Segmentation for Deforestation and Forest Fire DetectionJoe Soundararajan0Andrew Kalukin1Jordan Malof2Dong Xu3Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65201, USAThrough Sensing LLC, Arlington, VA 22201, USADepartment of Electrical Engineering and Computer Science, College of Engineering, University of Missouri, Columbia, MO 65201, USAInstitute for Data Science and Informatics, University of Missouri, Columbia, MO 65201, USADeforestation and forest fires are escalating global threats that require timely, scalable, and cost-effective monitoring systems. While UAV and ground-based solutions offer fine-grained data, they are often constrained by limited spatial coverage, high operational costs, and logistical challenges. In contrast, satellite imagery provides broad, repeatable, and economically feasible coverage. This study presents a deep learning framework that combines the DeepLabV3+ architecture with an EfficientNet-B08 backbone to address both deforestation and wildfire detection using satellite imagery. The system utilizes advanced multi-scale feature extraction and Group Normalization to enable robust semantic segmentation under challenging atmospheric conditions and complex forest structures. It is evaluated on two benchmark datasets. In the Amazon forest segmentation dataset, the model achieves a validation Intersection over Union (IoU) of 0.9100 and a pixel accuracy of 0.9605, demonstrating strong performance in delineating forest boundaries. In FireDataset_20m, which presents a severe class imbalance between fire and non-fire pixels, the framework achieves 99.95% accuracy, 93.16% precision, and 91.47% recall. A qualitative analysis confirms the model’s ability to accurately localize fire hotspots and deforested areas. These results highlight the model’s dual-purpose utility for high-resolution, multi-temporal environmental monitoring. Its balanced performance across metrics and adaptability to complex terrain conditions make it a promising tool for supporting forest conservation, early fire detection, and evidence-based policy interventions.https://www.mdpi.com/2072-4292/17/14/2333DeepLabV3+EfficientNet-B08deforestation detectionwildfire detectionsemantic segmentationsatellite imagery |
| spellingShingle | Joe Soundararajan Andrew Kalukin Jordan Malof Dong Xu Deep Learning-Driven Multi-Temporal Detection: Leveraging DeeplabV3+/Efficientnet-B08 Semantic Segmentation for Deforestation and Forest Fire Detection Remote Sensing DeepLabV3+ EfficientNet-B08 deforestation detection wildfire detection semantic segmentation satellite imagery |
| title | Deep Learning-Driven Multi-Temporal Detection: Leveraging DeeplabV3+/Efficientnet-B08 Semantic Segmentation for Deforestation and Forest Fire Detection |
| title_full | Deep Learning-Driven Multi-Temporal Detection: Leveraging DeeplabV3+/Efficientnet-B08 Semantic Segmentation for Deforestation and Forest Fire Detection |
| title_fullStr | Deep Learning-Driven Multi-Temporal Detection: Leveraging DeeplabV3+/Efficientnet-B08 Semantic Segmentation for Deforestation and Forest Fire Detection |
| title_full_unstemmed | Deep Learning-Driven Multi-Temporal Detection: Leveraging DeeplabV3+/Efficientnet-B08 Semantic Segmentation for Deforestation and Forest Fire Detection |
| title_short | Deep Learning-Driven Multi-Temporal Detection: Leveraging DeeplabV3+/Efficientnet-B08 Semantic Segmentation for Deforestation and Forest Fire Detection |
| title_sort | deep learning driven multi temporal detection leveraging deeplabv3 efficientnet b08 semantic segmentation for deforestation and forest fire detection |
| topic | DeepLabV3+ EfficientNet-B08 deforestation detection wildfire detection semantic segmentation satellite imagery |
| url | https://www.mdpi.com/2072-4292/17/14/2333 |
| work_keys_str_mv | AT joesoundararajan deeplearningdrivenmultitemporaldetectionleveragingdeeplabv3efficientnetb08semanticsegmentationfordeforestationandforestfiredetection AT andrewkalukin deeplearningdrivenmultitemporaldetectionleveragingdeeplabv3efficientnetb08semanticsegmentationfordeforestationandforestfiredetection AT jordanmalof deeplearningdrivenmultitemporaldetectionleveragingdeeplabv3efficientnetb08semanticsegmentationfordeforestationandforestfiredetection AT dongxu deeplearningdrivenmultitemporaldetectionleveragingdeeplabv3efficientnetb08semanticsegmentationfordeforestationandforestfiredetection |