Optimizing Deep Learning Models for Climate-Related Natural Disaster Detection from UAV Images and Remote Sensing Data
| Title: | Optimizing Deep Learning Models for Climate-Related Natural Disaster Detection from UAV Images and Remote Sensing Data |
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| Authors: | Kim VanExel, Samendra Sherchan, Siyan Liu |
| Source: | Journal of Imaging, Vol 11, Iss 2, p 32 (2025) |
| Publisher Information: | MDPI AG, 2025. |
| Publication Year: | 2025 |
| Collection: | LCC:Computer applications to medicine. Medical informatics LCC:Electronic computers. Computer science |
| Subject Terms: | AI, climate change, flooding, desertification, neural networks, CNN, Photography, TR1-1050, Computer applications to medicine. Medical informatics, R858-859.7, Electronic computers. Computer science, QA75.5-76.95 |
| More Details: | This research study utilized artificial intelligence (AI) to detect natural disasters from aerial images. Flooding and desertification were two natural disasters taken into consideration. The Climate Change Dataset was created by compiling various open-access data sources. This dataset contains 6334 aerial images from UAV (unmanned aerial vehicles) images and satellite images. The Climate Change Dataset was then used to train Deep Learning (DL) models to identify natural disasters. Four different Machine Learning (ML) models were used: convolutional neural network (CNN), DenseNet201, VGG16, and ResNet50. These ML models were trained on our Climate Change Dataset so that their performance could be compared. DenseNet201 was chosen for optimization. All four ML models performed well. DenseNet201 and ResNet50 achieved the highest testing accuracies of 99.37% and 99.21%, respectively. This research project demonstrates the potential of AI to address environmental challenges, such as climate change-related natural disasters. This study’s approach is novel by creating a new dataset, optimizing an ML model, cross-validating, and presenting desertification as one of our natural disasters for DL detection. Three categories were used (Flooded, Desert, Neither). Our study relates to AI for Climate Change and Environmental Sustainability. Drone emergency response would be a practical application for our research project. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 11020032 2313-433X |
| Relation: | https://www.mdpi.com/2313-433X/11/2/32; https://doaj.org/toc/2313-433X |
| DOI: | 10.3390/jimaging11020032 |
| Access URL: | https://doaj.org/article/dc981b4b921e4f90a77773f2cb13df53 |
| Accession Number: | edsdoj.981b4b921e4f90a77773f2cb13df53 |
| Database: | Directory of Open Access Journals |
| ISSN: | 11020032 2313433X |
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| DOI: | 10.3390/jimaging11020032 |
| Published in: | Journal of Imaging |
| Language: | English |