85 citations · 367 across the 22 of their papers we have counts for
22 papers
PolyGNN: Polyhedron-based Graph Neural Network for 3D Building Reconstruction from Point Clouds
Zhaiyu Chen, Yilei Shi, Liangliang Nan +2
We present PolyGNN, a polyhedron-based graph neural network for 3D building reconstruction from point clouds. PolyGNN learns to assemble primitives obtained by polyhedral decomposi…
RSSOD-Bench: A large-scale benchmark dataset for Salient Object Detection in Optical Remote Sensing Imagery
Zhitong Xiong, Yanfeng Liu, Qi Wang +1
We present the RSSOD-Bench dataset for salient object detection (SOD) in optical remote sensing imagery. While SOD has achieved success in natural scene images with deep learning,…
GEO-Bench: Toward Foundation Models for Earth Monitoring
Alexandre Lacoste, Nils Lehmann, Pau Rodriguez +14
Recent progress in self-supervision has shown that pre-training large neural networks on vast amounts of unsupervised data can lead to substantial increases in generalization to do…
Basis Pursuit Denoising via Recurrent Neural Network Applied to Super-resolving SAR Tomography
Kun Qian, Yuanyuan Wang, Peter Jung +2
Finding sparse solutions of underdetermined linear systems commonly requires the solving of L1 regularized least squares minimization problem, which is also known as the basis purs…
Vision-Language Models in Remote Sensing: Current Progress and Future Trends
Xiang Li, Congcong Wen, Yuan Hu +2
The remarkable achievements of ChatGPT and GPT-4 have sparked a wave of interest and research in the field of large language models for Artificial General Intelligence (AGI). These…
UnCRtainTS: Uncertainty Quantification for Cloud Removal in Optical Satellite Time Series
Patrick Ebel, Vivien Sainte Fare Garnot, Michael Schmitt +2
Clouds and haze often occlude optical satellite images, hindering continuous, dense monitoring of the Earth's surface. Although modern deep learning methods can implicitly learn to…
Geo-Information Harvesting from Social Media Data
Xiao Xiang Zhu, Yuanyuan Wang, Mrinalini Kochupillai +9
As unconventional sources of geo-information, massive imagery and text messages from open platforms and social media form a temporally quasi-seamless, spatially multi-perspective s…
Exploring Self-Attention for Crop-type Classification Explainability
Ivica Obadic, Ribana Roscher, Dario Augusto Borges Oliveira +1
Transformer models have become a promising approach for crop-type classification. Although their attention weights can be used to understand the relevant time points for crop disam…
Anomaly Detection in Aerial Videos with Transformers
Pu Jin, Lichao Mou, Gui-Song Xia +1
Unmanned aerial vehicles (UAVs) are widely applied for purposes of inspection, search, and rescue operations by the virtue of low-cost, large-coverage, real-time, and high-resoluti…
FuTH-Net: Fusing Temporal Relations and Holistic Features for Aerial Video Classification
Pu Jin, Lichao Mou, Yuansheng Hua +2
Unmanned aerial vehicles (UAVs) are now widely applied to data acquisition due to its low cost and fast mobility. With the increasing volume of aerial videos, the demand for automa…
The Outcome of the 2022 Landslide4Sense Competition: Advanced Landslide Detection from Multi-Source Satellite Imagery
Omid Ghorbanzadeh, Yonghao Xu, Hengwei Zhao +12
The scientific outcomes of the 2022 Landslide4Sense (L4S) competition organized by the Institute of Advanced Research in Artificial Intelligence (IARAI) are presented here. The obj…
HPO: We won't get fooled again
Kalifou René Traoré, Andrés Camero, Xiao Xiang Zhu
Hyperparameter optimization (HPO) is a well-studied research field. However, the effects and interactions of the components in an HPO pipeline are not yet well investigated. Then,…
From Easy to Hard: Learning Language-guided Curriculum for Visual Question Answering on Remote Sensing Data
Zhenghang Yuan, Lichao Mou, Qi Wang +1
Visual question answering (VQA) for remote sensing scene has great potential in intelligent human-computer interaction system. Although VQA in computer vision has been widely resea…
DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation
Aysim Toker, Lukas Kondmann, Mark Weber +10
Earth observation is a fundamental tool for monitoring the evolution of land use in specific areas of interest. Observing and precisely defining change, in this context, requires b…
Using Social Media Images for Building Function Classification
Eike Jens Hoffmann, Karam Abdulahhad, Xiao Xiang Zhu
Urban land use on a building instance level is crucial geo-information for many applications, yet difficult to obtain. An intuitive approach to close this gap is predicting buildin…
A Data-driven Approach to Neural Architecture Search Initialization
Kalifou René Traoré, Andrés Camero, Xiao Xiang Zhu
Algorithmic design in neural architecture search (NAS) has received a lot of attention, aiming to improve performance and reduce computational cost. Despite the great advances made…
Fitness Landscape Footprint: A Framework to Compare Neural Architecture Search Problems
Kalifou René Traoré, Andrés Camero, Xiao Xiang Zhu
Neural architecture search is a promising area of research dedicated to automating the design of neural network models. This field is rapidly growing, with a surge of methodologies…
Lessons from the Clustering Analysis of a Search Space: A Centroid-based Approach to Initializing NAS
Kalifou Rene Traore, Andrés Camero, Xiao Xiang Zhu
Lots of effort in neural architecture search (NAS) research has been dedicated to algorithmic development, aiming at designing more efficient and less costly methods. Nonetheless,…
Segmentation of VHR EO Images using Unsupervised Learning
Sudipan Saha, Lichao Mou, Muhammad Shahzad +1
Semantic segmentation is a crucial step in many Earth observation tasks. Large quantity of pixel-level annotation is required to train deep networks for semantic segmentation. Eart…
Under the Radar -- Auditing Fairness in ML for Humanitarian Mapping
Lukas Kondmann, Xiao Xiang Zhu
Humanitarian mapping from space with machine learning helps policy-makers to timely and accurately identify people in need. However, recent concerns around fairness and transparenc…
Self-supervised Audiovisual Representation Learning for Remote Sensing Data
Konrad Heidler, Lichao Mou, Di Hu +5
Many current deep learning approaches make extensive use of backbone networks pre-trained on large datasets like ImageNet, which are then fine-tuned to perform a certain task. In r…
So2Sat LCZ42: A Benchmark Dataset for Global Local Climate Zones Classification
Xiao Xiang Zhu, Jingliang Hu, Chunping Qiu +14
Access to labeled reference data is one of the grand challenges in supervised machine learning endeavors. This is especially true for an automated analysis of remote sensing images…