EarthLD: Towards Unified Open-World Landslide Understanding via Vision-Language Guided Diffusion Models

• URL: http://arxiv.org/abs/2609.00712v1
• Date: Tue, 01 Sep 2026 04:41:50 GMT
• Title: EarthLD: Towards Unified Open-World Landslide Understanding via Vision-Language Guided Diffusion Models
• Abstract summary: We propose EarthLD, a vision-language-guided diffusion framework for open-world landslide understanding.<n>At its core, EarthLD formulates landslide understanding as a diffusion process that progressively infers the presence, spatial extent, and pixel-level boundaries of landslides.<n>By integrating visual observations with contextual knowledge in the denoising process, EarthLD distinguishes diverse landslides from backgrounds, produces confidence-aware predictions for suspected regions, and maps landslide ranges.
• Score: 37.508845852069676
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Landslides are widespread geological hazards, yet their automated detection and mapping in remote sensing imagery remain challenging because of their irregular morphology, ambiguous spectral signatures, and substantial domain shifts across imaging platforms. To overcome these challenges, we propose EarthLD, a vision-language-guided diffusion framework for open-world landslide understanding, enabling unified landslide recognition, mapping, and trigger interpretation. At its core, EarthLD formulates landslide understanding as a diffusion process that progressively infers the presence, spatial extent, and pixel-level boundaries of landslides from noisy latent representations. This probabilistic formulation enables the model to jointly perform image-level landslide recognition and mapping while characterizing predictive uncertainty. By integrating visual observations with contextual knowledge in the denoising process, EarthLD distinguishes diverse landslides from backgrounds, produces confidence-aware predictions for suspected regions, and maps landslide ranges. We additionally construct a global-scale open-world landslide benchmark by systematically harmonizing multiple publicly available remote sensing data collected by diverse institutions. Extensive experiments across regions, sensors, and triggering events demonstrate that EarthLD consistently outperforms existing landslide detection methods, highlighting its potential as a unified and robust solution for global geological-hazard monitoring and emergency response.

Related papers

• Global-Local Contextual Progressive Expansion Network for Martian Landslide Segmentation in Multimodal Remote Sensing Imagery [7.778724782015986]
  This work investigates the capability of deep learning to address Martian landslide segmentation.<n>We propose TransCPLES, a U-shaped network that couples Contextual Progressive Layer Expansion feature extraction with Transformer-based contextual reasoning.<n>Experiments on MMLSv2, a seven-band multimodal Martian landslide dataset, show that TransCPLES achieves the best overall performance.
  arXiv  Detail & Related papers  (2026-09-11T09:24:53Z)
• RAEM: Robust Autonomous Exploration for Multi-Floor Environments with a Quadruped Robot [41.3871843269565]
  RAEM is a robust autonomous exploration framework for quadruped robots operating in multi-floor environments.<n>We introduce a staircase center alignment strategy to reduce abrupt yaw variations during climbing.
  arXiv  Detail & Related papers  (2026-08-26T04:40:34Z)
• Earth-o1: A Grid-free Observation-native Atmospheric World Model [81.55899748753434]
  We present Earth-o1, an observation-native atmospheric world model that overcomes structural limitations.<n>By integrating diverse sensor inputs into a unified, grid-free dynamical field, the model autonomously advances the atmospheric state in space and time.<n>We show that this fundamentally distinct paradigm enables direct, real-time forecasting and cross-sensor inference without the overhead of explicit numerical solvers.
  arXiv  Detail & Related papers  (2026-05-07T14:27:48Z)
• FlowEO: Generative Unsupervised Domain Adaptation for Earth Observation [8.162386935076237]
  FlowEO is a novel framework that leverages generative models for image-space UDA in Earth observation.<n>We conduct experiments across four datasets covering adaptation scenarios such as SAR to optical translation and temporal and semantic shifts caused by natural disasters.<n>Results demonstrate that FlowEO outperforms existing image translation approaches for domain adaptation while achieving on-par or better perceptual image quality.
  arXiv  Detail & Related papers  (2025-12-01T10:29:01Z)
• Annotation-Free Open-Vocabulary Segmentation for Remote-Sensing Images [51.74614065919118]
  This paper introduces SegEarth-OV, the first framework for annotation-free open-vocabulary segmentation of RS images.<n>We propose SimFeatUp, a universal upsampler that robustly restores high-resolution spatial details from coarse features.<n>We also present a simple yet effective Global Bias Alleviation operation to subtract the inherent global context from patch features.
  arXiv  Detail & Related papers  (2025-08-25T14:22:57Z)
• Landslide Detection and Mapping Using Deep Learning Across Multi-Source Satellite Data and Geographic Regions [0.0]
  Landslides pose severe threats to infrastructure, economies, and human lives.<n>With advancements in deep learning and remote sensing, automated landslide detection has become increasingly effective.<n>This study presents a comprehensive approach integrating multi-source satellite imagery and deep learning models to enhance landslide identification and prediction.
  arXiv  Detail & Related papers  (2025-07-01T18:34:42Z)
• TerraFM: A Scalable Foundation Model for Unified Multisensor Earth Observation [65.74990259650984]
  We introduce TerraFM, a scalable self-supervised learning model that leverages globally distributed Sentinel-1 and Sentinel-2 imagery.<n>Our training strategy integrates local-global contrastive learning and introduces a dual-centering mechanism.<n>TerraFM achieves strong generalization on both classification and segmentation tasks, outperforming prior models on GEO-Bench and Copernicus-Bench.
  arXiv  Detail & Related papers  (2025-06-06T17:59:50Z)
• Ground-R1: Incentivizing Grounded Visual Reasoning via Reinforcement Learning [96.01617809845396]
  Ground-R1 is a reinforcement learning framework that enables grounded visual reasoning without requiring explicit evidence or rationale annotations.<n>Ground-R1 achieves superior performance and exhibits emergent cognitive behaviors such as uncertainty awareness, spatial perception, and iterative refinement.
  arXiv  Detail & Related papers  (2025-05-26T17:51:47Z)
• No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data [13.412573082645096]
  Implicit neural representations (INRs) exhibit growing promise in addressing Earth representation challenges.<n>Existing methods disproportionately prioritize global average performance.<n>We introduce FAIR-Earth: a first-of-its-kind dataset to examine and challenge inequities in Earth representations.
  arXiv  Detail & Related papers  (2025-02-05T16:51:13Z)
• View Consistent Purification for Accurate Cross-View Localization [59.48131378244399]
  This paper proposes a fine-grained self-localization method for outdoor robotics. The proposed method addresses limitations in existing cross-view localization methods. It is the first sparse visual-only method that enhances perception in dynamic environments.
  arXiv  Detail & Related papers  (2023-08-16T02:51:52Z)
• DFPENet-geology: A Deep Learning Framework for High Precision Recognition and Segmentation of Co-seismic Landslides [7.927831418004974]
  This paper develops a robust model, Dense Feature Pyramid with Dense-decoder Network (DFPENet) to understand and fuse the multi-scale features of objects in remote sensing images. A comprehensive and widely-used scheme is proposed for co-seismic landslide recognition, which integrates image features extracted from the DFPENet model, geologic features, temporal resolution, landslide spatial analysis, and transfer learning.
  arXiv  Detail & Related papers  (2019-08-28T19:07:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.