Newly triggered landslides rarely carry immediate annotations, so cross-domain transferability determines the value of landslide mapping for emergency response and regional risk assessment. Vision foundation models have strengthened representational transfer, yet on unseen regions, events, and data sources they still generate high-confidence false alarms.
The paper introduces a Local-Geo and Spatial Context Fusion (LGSCF) strategy that combines point-based geo-environmental features with surrounding spatial context using a feature-wise modulation mechanism. Applied to nine CNN models over a 2644 km² area in Taiwan, LGSCF consistently outperforms baseline models, achieving F1-scores up to 87.09% and AUC values up to 0.9472. The resulting susceptibility maps more accurately concentrate known landslides in high-risk zones with fewer misclassifications.
By Yusen Cheng, Lei Fan, Qinfeng Zhu, Cheng Zhang, Yangyang Li, Ron Mahabir
arXiv:2606. 14081v2 Announce Type: replace-cross Abstract: Rapid post-event landslide mapping is essential for disaster response but remains difficult to automate due to extreme class imbalance.
By Huong Binh Vu
arXiv:2606. 14760v1 Announce Type: cross Abstract: Remote-sensing foundation models (RSFMs) benefit from pretraining on imagery from multiple sensors and ground sampling distances (GSDs), but such exposure alone does not resolve scale mismatch during downstream adaptation.
By Yu Luo, Kun Hu, Mengwei He, Xiaogang Zhu, Shan Zeng, Allen Benter, Wei Xiang, Patrick Filippi, Thomas Francis Bishop, Zhiyong Wang
arXiv:2606. 14081v1 Announce Type: cross Abstract: Rapid post-event landslide mapping is essential for disaster response but remains difficult to automate due to extreme class imbalance.
By Huong Binh Vu
Timely, high-resolution maps of flood extent around settlements are essential for emergency response and damage assessment. We consider airborne RGB imagery for flood mapping as it can be collected rapidly at low cost.
arXiv:2609.00712v1 Announce Type: new
Abstract: Landslides are widespread geological hazards, yet their automated detection and mapping in remote sensing imagery remain challenging because of their i...
By Yuanchao Su, Lianru Gao, Mengying Jiang, Jiangyi Chen, Jiaxin Cheng, Yicong Zhou
arXiv:2601.07268v2 Announce Type: replace
Abstract: Data-driven landslide susceptibility mapping (LSM) typically relies on landslide conditioning factors (LCFs), whose availability, heterogeneity, an...
By Yusen Cheng, Qinfeng Zhu, Lei Fan
arXiv:2607. 16050v1 Announce Type: new Abstract: Pluvial (rainfall-driven) flooding accounts for 45% of National Flood Insurance Program (NFIP) claims in the United States and is harder to predict than its riverine and coastal counterparts, with existing approaches limited to coarse resolution, regional domains, or computationally intensive process-based models unsuitable for daily continental-scale use.
By Yuya Kawakami, Daniel Cayan, Dongyu Liu, Kwan-Liu Ma, Tom Corringham
The study evaluates the use of frozen geospatial foundation embeddings (AlphaEarth) for mapping cultivated versus non‑cultivated land in Maine. Using 192 spatially separated patches and USDA Cropland Data Layer labels, a lightweight classifier achieved 93.7% overall accuracy without fine‑tuning, and a nearest‑class‑centroid rule reached 90.2%. A balanced sample of 60,000 labeled pixels was nearly as effective as the full 8.6 million‑pixel pool, and classifiers trained in one year remained accurate across 2018‑2023. In a blind human validation of 385 points, the AlphaEarth‑plus‑random‑forest map matched 95.3% of the consensus, outperforming the CDL reference (91.7%).
By Mohammad Ammar Mughees, Giovanni Montefoschi, Zhongxin Chen, Maria Antonia Brovelli
arXiv:2608. 08321v1 Announce Type: new Abstract: Floods and landslides often co-occur, but their relationships with environmental controls vary spatially.
By Aswathi Mundayatt, Siddharth Anil, Hitanshu Seth, Jaya Sreevalsan-Nair
arXiv:2606. 07780v1 Announce Type: new Abstract: Floods are among the most destructive natural hazards, and their increasing frequency under climate change makes satellite-based inundation mapping essential for disaster response.
By Venkatesh Kolluru, Rajat Shinde, Abdelhak Marouane, Caden Helbling, Deepak Shah, Othneil Drew, Iksha Gurung, Manil Maskey, Rahul Ramachandran