Harnessing Geospatial Artificial Intelligence and Deep Learning for Landslide Inventory Mapping: Advances, Challenges, and Emerging Directions
Recent advancements in artificial intelligence (AI) and deep learning enable more accurate, scalable, and automated mapping. This paper provides a comprehensive review of the applications of AI, particularly deep learning, in landslide inventory mapping. In addition to examining commonly used data s...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/11/1856 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849330708622868480 |
|---|---|
| author | Xiao Chen Wenwen Li Chia-Yu Hsu Samantha T. Arundel Bretwood Higman |
| author_facet | Xiao Chen Wenwen Li Chia-Yu Hsu Samantha T. Arundel Bretwood Higman |
| author_sort | Xiao Chen |
| collection | DOAJ |
| description | Recent advancements in artificial intelligence (AI) and deep learning enable more accurate, scalable, and automated mapping. This paper provides a comprehensive review of the applications of AI, particularly deep learning, in landslide inventory mapping. In addition to examining commonly used data sources and model architectures, we explore innovative strategies such as feature enhancement and fusion, attention-boosted techniques, and advanced learning approaches, including active learning and transfer learning, to enhance model adaptability and predictability. We also highlight the remaining challenges and potential research directions, including the estimation of more diverse variables in landslide mapping, multimodal data alignment, modeling regional variability and replicability, as well as issues related to data misinterpretation and model explainability. This review aims to serve as a useful resource for researchers and practitioners, promoting the integration of deep learning into landslide research and disaster management. |
| format | Article |
| id | doaj-art-77ee92ada22f4964a6845752d6ff07c5 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-77ee92ada22f4964a6845752d6ff07c52025-08-20T03:46:49ZengMDPI AGRemote Sensing2072-42922025-05-011711185610.3390/rs17111856Harnessing Geospatial Artificial Intelligence and Deep Learning for Landslide Inventory Mapping: Advances, Challenges, and Emerging DirectionsXiao Chen0Wenwen Li1Chia-Yu Hsu2Samantha T. Arundel3Bretwood Higman4School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ 85287, USASchool of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ 85287, USASchool of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ 85287, USAU.S. Geological Survey, Center of Excellence for Geospatial Information Science (CEGIS), Rolla, MO 65401, USAGround Truth Alaska, Seldovia, AK 99663, USARecent advancements in artificial intelligence (AI) and deep learning enable more accurate, scalable, and automated mapping. This paper provides a comprehensive review of the applications of AI, particularly deep learning, in landslide inventory mapping. In addition to examining commonly used data sources and model architectures, we explore innovative strategies such as feature enhancement and fusion, attention-boosted techniques, and advanced learning approaches, including active learning and transfer learning, to enhance model adaptability and predictability. We also highlight the remaining challenges and potential research directions, including the estimation of more diverse variables in landslide mapping, multimodal data alignment, modeling regional variability and replicability, as well as issues related to data misinterpretation and model explainability. This review aims to serve as a useful resource for researchers and practitioners, promoting the integration of deep learning into landslide research and disaster management.https://www.mdpi.com/2072-4292/17/11/1856landslide detectionGeoAIremote sensing |
| spellingShingle | Xiao Chen Wenwen Li Chia-Yu Hsu Samantha T. Arundel Bretwood Higman Harnessing Geospatial Artificial Intelligence and Deep Learning for Landslide Inventory Mapping: Advances, Challenges, and Emerging Directions Remote Sensing landslide detection GeoAI remote sensing |
| title | Harnessing Geospatial Artificial Intelligence and Deep Learning for Landslide Inventory Mapping: Advances, Challenges, and Emerging Directions |
| title_full | Harnessing Geospatial Artificial Intelligence and Deep Learning for Landslide Inventory Mapping: Advances, Challenges, and Emerging Directions |
| title_fullStr | Harnessing Geospatial Artificial Intelligence and Deep Learning for Landslide Inventory Mapping: Advances, Challenges, and Emerging Directions |
| title_full_unstemmed | Harnessing Geospatial Artificial Intelligence and Deep Learning for Landslide Inventory Mapping: Advances, Challenges, and Emerging Directions |
| title_short | Harnessing Geospatial Artificial Intelligence and Deep Learning for Landslide Inventory Mapping: Advances, Challenges, and Emerging Directions |
| title_sort | harnessing geospatial artificial intelligence and deep learning for landslide inventory mapping advances challenges and emerging directions |
| topic | landslide detection GeoAI remote sensing |
| url | https://www.mdpi.com/2072-4292/17/11/1856 |
| work_keys_str_mv | AT xiaochen harnessinggeospatialartificialintelligenceanddeeplearningforlandslideinventorymappingadvanceschallengesandemergingdirections AT wenwenli harnessinggeospatialartificialintelligenceanddeeplearningforlandslideinventorymappingadvanceschallengesandemergingdirections AT chiayuhsu harnessinggeospatialartificialintelligenceanddeeplearningforlandslideinventorymappingadvanceschallengesandemergingdirections AT samanthatarundel harnessinggeospatialartificialintelligenceanddeeplearningforlandslideinventorymappingadvanceschallengesandemergingdirections AT bretwoodhigman harnessinggeospatialartificialintelligenceanddeeplearningforlandslideinventorymappingadvanceschallengesandemergingdirections |