# Landslide4Sense: Reference Benchmark Data and Deep Learning Models for Landslide Detection

arXiv: https://arxiv.org/abs/2206.00515 · HTML: https://mlsanity.com/paper/2206.00515/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 92 references that are in the map.
- First posted on arXiv 1 Jun 2022.

## Builds on (most cited first, 19 of 19)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Road Extraction by Deep Residual U-Net](https://mlsanity.com/paper/1711.10684/) (2017)
- [LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2110.08733/) (2021)
- [Deep Learning: A Critical Appraisal](https://mlsanity.com/paper/1801.00631/) (2018)
- [LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation](https://mlsanity.com/paper/1707.03718/) (2017)
- [Strike (with) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects](https://mlsanity.com/paper/1811.11553/) (2018)
- [Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes](https://mlsanity.com/paper/1611.08323/) (2016)
- [ContextNet: Exploring Context and Detail for Semantic Segmentation in Real-time](https://mlsanity.com/paper/1805.04554/) (2018)
- [Remote Sensing Image Super-resolution and Object Detection: Benchmark and State of the Art](https://mlsanity.com/paper/2111.03260/) (2021)
- [Vehicle Instance Segmentation from Aerial Image and Video Using a Multi-Task Learning Residual Fully Convolutional Network](https://mlsanity.com/paper/1805.10485/) (2018)
- [A New Mask R-CNN Based Method for Improved Landslide Detection](https://mlsanity.com/paper/2010.01499/) (2020)
- [Landslide Segmentation with U-Net: Evaluating Different Sampling Methods and Patch Sizes](https://mlsanity.com/paper/2007.06672/) (2020)
- [Improving Landslide Detection on SAR Data through Deep Learning](https://mlsanity.com/paper/2105.00782/) (2021)
- [Breaking the Limits of Remote Sensing by Simulation and Deep Learning for Flood and Debris Flow Mapping](https://mlsanity.com/paper/2006.05180/) (2020)

## Built on by (most cited first, 26 of 26)

- [BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response](https://mlsanity.com/paper/2501.06019/) (2025)
- [Txt2Img-MHN: Remote Sensing Image Generation from Text Using Modern Hopfield Networks](https://mlsanity.com/paper/2208.04441/) (2022)
- [The Outcome of the 2022 Landslide4Sense Competition: Advanced Landslide Detection from Multi-Source Satellite Imagery](https://mlsanity.com/paper/2209.02556/) (2022)
- [Anomaly Segmentation for High-Resolution Remote Sensing Images Based on Pixel Descriptors](https://mlsanity.com/paper/2301.13422/) (2023)
- [MS-Former: Memory-Supported Transformer for Weakly Supervised Change Detection with Patch-Level Annotations](https://mlsanity.com/paper/2311.09726/) (2023)
- [Tinto: Multisensor Benchmark for 3D Hyperspectral Point Cloud Segmentation in the Geosciences](https://mlsanity.com/paper/2305.09928/) (2023)
- [Auto-Prompting SAM for Weakly Supervised Landslide Extraction](https://mlsanity.com/paper/2501.13426/) (2025)
- [Landslide Detection and Segmentation Using Remote Sensing Images and Deep Neural Network](https://mlsanity.com/paper/2312.16717/) (2023)
- [PCB-Vision: A Multiscene RGB-Hyperspectral Benchmark Dataset of Printed Circuit Boards](https://mlsanity.com/paper/2401.06528/) (2024)
- [Towards Efficient Benchmarking of Foundation Models in Remote Sensing: A Capabilities Encoding Approach](https://mlsanity.com/paper/2505.03299/) (2025)
- [A Comparative Analysis of CNN-based Deep Learning Models for Landslide Detection](https://mlsanity.com/paper/2408.01692/) (2024)
- [RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images](https://mlsanity.com/paper/2507.11143/) (2025)
- [Landslide Detection and Mapping Using Deep Learning Across Multi-Source Satellite Data and Geographic Regions](https://mlsanity.com/paper/2507.01123/) (2025)
- [Landslide Hazard Mapping with Geospatial Foundation Models: Geographical Generalizability, Data Scarcity, and Band Adaptability](https://mlsanity.com/paper/2511.04474/) (2025)
- [First On-Orbit Demonstration of a Geospatial Foundation Model](https://mlsanity.com/paper/2512.01181/) (2025)
- [DualSwinFusionSeg: Multimodal Martian Landslide Segmentation via Dual Swin Transformer with Multi-Scale Fusion and UNet++](https://mlsanity.com/paper/2603.14132/) (2026)
- [Clay-CNN Hybrids: Leveraging Geospatial Foundation Models as Auxiliary Context for Landslide Detection](https://mlsanity.com/paper/2606.14081/) (2026)
- [The impact of the AI revolution on asset management](https://mlsanity.com/paper/2304.10212/) (2023)
- [Few-shot Multispectral Segmentation with Representations Generated by Reinforcement Learning](https://mlsanity.com/paper/2311.11827/) (2023)
- [LMHLD: A Large-scale Multi-source High-resolution Landslide Dataset for Landslide Detection based on Deep Learning](https://mlsanity.com/paper/2502.19866/) (2025)
- [Survey on Disaster Management Datasets for Remote Sensing Based Emergency Applications](https://mlsanity.com/paper/2605.08196/) (2026)
- [Sequential Feature Selection for Efficient Landslide Segmentation from Multi-Spectral Data](https://mlsanity.com/paper/2605.09746/) (2026)
- [NGram-MoSE: Efficient Remote Sensing Super-Resolution via N-Gram Context and Mixture-of-Experts](https://mlsanity.com/paper/2606.08535/) (2026)
- [LandslideAgent with Multimodal LandslideBench: A Domain-Rule-Augmented Agent for Autonomous Landslide Identification and Analysis](https://mlsanity.com/paper/2606.18661/) (2026)
- [Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge](https://mlsanity.com/paper/2607.22746/) (2026)
- [SPECTRA: Band-Routed Embedding and Stage-Wise LoRA for Cross-Sensor Fine-Tuning of Geospatial Foundation Models](https://mlsanity.com/paper/2608.01751/) (2026)

## Abstract (arXiv)

This study introduces textitLandslide4Sense, a reference benchmark for landslide detection from remote sensing. The repository features 3,799 image patches fusing optical layers from Sentinel-2 sensors with the digital elevation model and slope layer derived from ALOS PALSAR. The added topographical information facilitates the accurate detection of landslide borders, which recent researches have shown to be challenging using optical data alone. The extensive data set supports deep learning (DL) studies in landslide detection and the development and validation of methods for the systematic update of landslide inventories. The benchmark data set has been collected at four different times and geographical locations: Iburi (September 2018), Kodagu (August 2018), Gorkha (April 2015), and Taiwan (August 2009). Each image pixel is labelled as belonging to a landslide or not, incorporating various sources and thorough manual annotation. We then evaluate the landslide detection performance of 11 state-of-the-art DL segmentation models: U-Net, ResU-Net, PSPNet, ContextNet, DeepLab-v2, DeepLab-v3+, FCN-8s, LinkNet, FRRN-A, FRRN-B, and SQNet. All models were trained from scratch on patches from one quarter of each study area and tested on independent patches from the other three quarters. Our experiments demonstrate that ResU-Net outperformed the other models for the landslide detection task. We make the multi-source landslide benchmark data (Landslide4Sense) and the tested DL models publicly available at urlhttps://www.iarai.ac.at/landslide4sense, establishing an important resource for remote sensing, computer vision, and machine learning communities in studies of image classification in general and applications to landslide detection in particular.