Integrating DEM and Deep Learning for Forested Terrain Analysis: Enhancing Fire Risk Assessment Through Mountain Peak and Water System Extraction in Chongli District DOI Open Access

Yihui Wu,

Xueying Sun, Liang Qi

et al.

Forests, Journal Year: 2025, Volume and Issue: 16(4), P. 692 - 692

Published: April 16, 2025

Accurate fire risk assessment in forested terrain is crucial for effective disaster management and ecological conservation. This study innovatively proposes a novel framework that integrates Digital Elevation Models (DEMs) with deep learning techniques to enhance Chongli District. Our combines DEM data Faster Regions Convolutional Neural Networks (Faster R-CNN) CNN-based methods, breaking through the limitations of traditional approaches rely on manual feature extraction. It capable automatically identifying critical features, such as mountain peaks water systems, higher accuracy efficiency. DEMs provide high-resolution topographical information, which models leverage accurately identify delineate key geographical features. results show integration significantly improves by offering detailed precise analysis, thereby providing more reliable inputs behavior prediction. The extracted fundamental prediction, enable accurate predictions spread potential impact areas. not only highlights great combining geospatial advanced machine but also offers scalable efficient solution forest mountainous regions. Future work will focus expanding dataset include environmental variables validating model different areas further its robustness applicability.

Language: Английский

Integrating DEM and Deep Learning for Forested Terrain Analysis: Enhancing Fire Risk Assessment Through Mountain Peak and Water System Extraction in Chongli District DOI Open Access

Yihui Wu,

Xueying Sun, Liang Qi

et al.

Forests, Journal Year: 2025, Volume and Issue: 16(4), P. 692 - 692

Published: April 16, 2025

Accurate fire risk assessment in forested terrain is crucial for effective disaster management and ecological conservation. This study innovatively proposes a novel framework that integrates Digital Elevation Models (DEMs) with deep learning techniques to enhance Chongli District. Our combines DEM data Faster Regions Convolutional Neural Networks (Faster R-CNN) CNN-based methods, breaking through the limitations of traditional approaches rely on manual feature extraction. It capable automatically identifying critical features, such as mountain peaks water systems, higher accuracy efficiency. DEMs provide high-resolution topographical information, which models leverage accurately identify delineate key geographical features. results show integration significantly improves by offering detailed precise analysis, thereby providing more reliable inputs behavior prediction. The extracted fundamental prediction, enable accurate predictions spread potential impact areas. not only highlights great combining geospatial advanced machine but also offers scalable efficient solution forest mountainous regions. Future work will focus expanding dataset include environmental variables validating model different areas further its robustness applicability.

Language: Английский

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