From Landslide Conditioning Factors to Satellite Embeddings: Evaluating the Utilisation of Google AlphaEarth for Landslide Susceptibility Mapping using Deep Learning

• URL: http://arxiv.org/abs/2601.07268v1
• Date: Mon, 12 Jan 2026 07:17:43 GMT
• Title: From Landslide Conditioning Factors to Satellite Embeddings: Evaluating the Utilisation of Google AlphaEarth for Landslide Susceptibility Mapping using Deep Learning
• Abstract summary: This study evaluated the potential of Google AlphaEarth embeddings as alternative predictors for landslide susceptibility mapping.<n>AE-based models consistently outperformed LCFs across all regions and models, yielding higher F1-scores, AUC values, and more stable error distributions.
• Score: 1.6571781613404601
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data-driven landslide susceptibility mapping (LSM) typically relies on landslide conditioning factors (LCFs), whose availability, heterogeneity, and preprocessing-related uncertainties can constrain mapping reliability. Recently, Google AlphaEarth (AE) embeddings, derived from multi-source geospatial observations, have emerged as a unified representation of Earth surface conditions. This study evaluated the potential of AE embeddings as alternative predictors for LSM. Two AE representations, including retained principal components and the full set of 64 embedding bands, were systematically compared with conventional LCFs across three study areas (Nantou County, Taiwan; Hong Kong; and part of Emilia-Romagna, Italy) using three deep learning models (CNN1D, CNN2D, and Vision Transformer). Performance was assessed using multiple evaluation metrics, ROC-AUC analysis, error statistics, and spatial pattern assessment. Results showed that AE-based models consistently outperformed LCFs across all regions and models, yielding higher F1-scores, AUC values, and more stable error distributions. Such improvement was most pronounced when using the full 64-band AE representation, with F1-score improvements of approximately 4% to 15% and AUC increased ranging from 0.04 to 0.11, depending on the study area and model. AE-based susceptibility maps also exhibited clearer spatial correspondence with observed landslide occurrences and enhanced sensitivity to localised landslide-prone conditions. Performance improvements were more evident in Nantou and Emilia than in Hong Kong, revealing that closer temporal alignment between AE embeddings and landslide inventories may lead to more effective LSM outcomes. These findings highlight the strong potential of AE embeddings as a standardised and information-rich alternative to conventional LCFs for LSM.

Related papers

• Multi-Sensor Mapping of Vulnerable Urban Settlements Using SAR, Multispectral, and Hyperspectral Imagery: A Case Study in Córdoba, Argentina [5.007870105030848]
  This work presents a multi-sensor deep learning framework for slum-likelihood mapping in Crdoba, Argentina.<n>It integrates high-resolution PlanetScope multispectral (MS) imagery, COSMO-SkyMed (CSK) Synthetic Aperture Radar (SAR) data, and medium-resolution PRISMA hyperspectral (HS) observations.<n>Results show that LF+HS provides the best overall balance between classification performance and spatial selectivity.
  arXiv  Detail & Related papers  (2026-08-26T07:05:36Z)
• Synergising Local Geo-Environmental Characteristics with Spatial Context for Enhancing Landslide Susceptibility Mapping [9.253806947597635]
  Local-Geo and Spatial Context Fusion (LGSCF) strategy synergises the geo-environmental characteristics of landslide points with their corresponding spatial context.<n>This study covers approximately 2644 km2 across Jenai and Sinyi Townships in Nantou County, Taiwan.
  arXiv  Detail & Related papers  (2026-08-25T02:32:10Z)
• Rethinking Attention Locality in Spiking Transformers [50.00501275379363]
  Spiking Transformers provide a promising paradigm for efficient visual processing with spike-driven computation.<n>Their Softmax-free Spiking Self-Attention (SSA) struggles to establish spatially localized token interactions.<n>We propose Spatially Contiguous Local Attention with Boundary Continuity Pathway (SCLA-BCP)<n>Our approach improves mAP@50 by up to 9.50% on COCO on 2017 and mIoU by up to 3.42% on ADE20K.
  arXiv  Detail & Related papers  (2026-08-09T07:36:17Z)
• Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting [52.53742727060265]
  Foundation models (FMs) for the Earth system learn statistical relationships between physical variables across massive datasets.<n>Here, we introduce Earth System Foundation Model (ESFM), a fully open model building on the 3Dwin UNet backbone of the pioneering model.<n>We present findings using dense gridded data (ERA5, CMIP6), regionally masked dense data, sparse gridded MODIS satellite data, and station data.<n>Results demonstrate competitive or superior performance relative to state-of-the-art benchmarks.
  arXiv  Detail & Related papers  (2026-04-20T11:40:39Z)
• FL-MHSM: Spatially-adaptive Fusion and Ensemble Learning for Flood-Landslide Multi-Hazard Susceptibility Mapping at Regional Scale [1.3607388598209322]
  This study proposes a deep learning (DL) workflow for joint flood-landslide multi-hazard susceptibility mapping (FL-MHSM)<n>It combines two-level spatial partitioning, probabilistic Early Fusion (EF), a tree-based Late Fusion (LF) baseline, and a soft-gating Mixture of Experts (MoE) model.<n>In Kerala, EF remained competitive with LF, improving flood recall from 0.816 to 0.840 and reducing Brier score from 0.092 to 0.086.<n>MoE provided strongest performance for flood susceptibility, achieving an AUC-ROC of 0.905, recall of 0.930, and
  arXiv  Detail & Related papers  (2026-04-17T17:23:55Z)
• Assessing airborne laser scanning and aerial photogrammetry for deep learning-based stand delineation [0.0]
  Accurate forest stand delineation is essential for forest inventory and management.<n>Deep learning can produce stand delineations comparable to expert interpreters when combining aerial imagery and airborne laser scanning (ALS) data.<n>Canopy height models (CHMs) derived from digital photogrammetry (DAP) offer better temporal alignment but may smoothen canopy surface and canopy gaps.<n>DTM has been suggested to improve delineation performance, but has remained untested in published literature.
  arXiv  Detail & Related papers  (2026-02-25T09:16:28Z)
• SAM-Aug: Leveraging SAM Priors for Few-Shot Parcel Segmentation in Satellite Time Series [3.4368348203064283]
  We propose SAM-Aug, a new annotation-efficient framework to improve few-shot land cover mapping.<n>Our approach constructs cloud-free composite images from temporal sequences and applies SAM in a fully unsupervised manner.<n>Experiments on the PASTIS-R benchmark under a 5 percent labeled setting demonstrate the effectiveness and robustness of SAM-Aug.
  arXiv  Detail & Related papers  (2026-01-14T03:18:04Z)
• Benchmarking foundation models for hyperspectral image classification: Application to cereal crop type mapping [0.9407085421584646]
  This study benchmarks three foundation models for cereal crop mapping using hyperspectral imagery.<n>Performance was measured with overall accuracy (OA), average accuracy (AA), and F1-score.
  arXiv  Detail & Related papers  (2025-10-13T16:21:59Z)
• Out-of-Distribution Generalization in Climate-Aware Yield Prediction with Earth Observation Data [0.0]
  We benchmark two state-of-the-art deep learning models, GNN-RNN and MMST-ViT, under realistic out-of-distribution conditions.<n>GNN-RNN demonstrates superior generalization with positive correlations under geographic shifts, while MMST-ViT performs well in-domain but degrades sharply under OOD conditions.
  arXiv  Detail & Related papers  (2025-10-08T03:27:12Z)
• A comparison of stretched-grid and limited-area modelling for data-driven regional weather forecasting [0.493599216374976]
  Limit-area model (LAM) and stretched-grid model (SGM) approaches have emerged for generating high-resolution regional forecasts.<n>This study aims to understand how the differences in model design impact relative performance and potential applications.
  arXiv  Detail & Related papers  (2025-07-24T12:54:08Z)
• ReconMOST: Multi-Layer Sea Temperature Reconstruction with Observations-Guided Diffusion [48.540756751934836]
  ReconMOST is a data-driven guided diffusion model framework for multi-layer sea temperature reconstruction.<n>Our method extends ML-based SST reconstruction to a global, multi-layer setting, handling over 92.5% missing data.
  arXiv  Detail & Related papers  (2025-06-12T06:27:22Z)
• 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)
• Distortions in Judged Spatial Relations in Large Language Models [45.875801135769585]
  GPT-4 exhibited superior performance with 55 percent accuracy, followed by GPT-3.5 at 47 percent, and Llama-2 at 45 percent. The models identified the nearest cardinal direction in most cases, reflecting their associative learning mechanism.
  arXiv  Detail & Related papers  (2024-01-08T20:08:04Z)
• Recognize Any Regions [55.76437190434433]
  RegionSpot integrates position-aware localization knowledge from a localization foundation model with semantic information from a ViL model.<n>Experiments in open-world object recognition show that our RegionSpot achieves significant performance gain over prior alternatives.
  arXiv  Detail & Related papers  (2023-11-02T16:31:49Z)
• Exploring Generalisability of Self-Distillation with No Labels for SAR-Based Vegetation Prediction [5.057850174013128]
  We pre-train a DINO-ViT based model using two Synthetic Aperture Radar datasets (S1GRD or GSSIC) across three regions (China, Conus, Europe) We fine-tune the models on smaller labeled datasets to predict vegetation percentage, and empirically study the connection between the embedding space of the models and their ability to generalize across diverse geographic regions and to unseen data.
  arXiv  Detail & Related papers  (2023-10-03T13:41:13Z)
• Long-term drought prediction using deep neural networks based on geospatial weather data [75.38539438000072]
  High-quality drought forecasting up to a year in advance is critical for agriculture planning and insurance. We tackle drought data by introducing an end-to-end approach that adopts a systematic end-to-end approach. Key findings are the exceptional performance of a Transformer model, EarthFormer, in making accurate short-term (up to six months) forecasts.
  arXiv  Detail & Related papers  (2023-09-12T13:28:06Z)
• Strict baselines for Covid-19 forecasting and ML perspective for USA and Russia [105.54048699217668]
  Covid-19 allows researchers to gather datasets accumulated over 2 years and to use them in predictive analysis. We present the results of a consistent comparative study of different types of methods for predicting the dynamics of the spread of Covid-19 based on regional data for two countries: the United States and Russia.
  arXiv  Detail & Related papers  (2022-07-15T18:21:36Z)
• Improving Visual Grounding by Encouraging Consistent Gradient-based Explanations [58.442103936918805]
  We show that Attention Mask Consistency produces superior visual grounding results than previous methods. AMC is effective, easy to implement, and is general as it can be adopted by any vision-language model.
  arXiv  Detail & Related papers  (2022-06-30T17:55:12Z)
• Jalisco's multiclass land cover analysis and classification using a novel lightweight convnet with real-world multispectral and relief data [51.715517570634994]
  We present our novel lightweight (only 89k parameters) Convolution Neural Network (ConvNet) to make LC classification and analysis. In this work, we combine three real-world open data sources to obtain 13 channels. Our embedded analysis anticipates the limited performance in some classes and gives us the opportunity to group the most similar.
  arXiv  Detail & Related papers  (2022-01-26T14:58:51Z)

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.