3.2k citations · 10k across the 87 of their papers we have counts for
89 papers
Cross-City Matters: A Multimodal Remote Sensing Benchmark Dataset for Cross-City Semantic Segmentation using High-Resolution Domain Adaptation Networks
Danfeng Hong, Bing Zhang, Hao Li +7
Artificial intelligence (AI) approaches nowadays have gained remarkable success in single-modality-dominated remote sensing (RS) applications, especially with an emphasis on indivi…
HTC-DC Net: Monocular Height Estimation from Single Remote Sensing Images
Sining Chen, Yilei Shi, Zhitong Xiong +1
3D geo-information is of great significance for understanding the living environment; however, 3D perception from remote sensing data, especially on a large scale, is restricted. T…
HyperLISTA-ABT: An Ultra-light Unfolded Network for Accurate Multi-component Differential Tomographic SAR Inversion
Kun Qian, Yuanyuan Wang, Peter Jung +2
Deep neural networks based on unrolled iterative algorithms have achieved remarkable success in sparse reconstruction applications, such as synthetic aperture radar (SAR) tomograph…
Self-supervised Domain-agnostic Domain Adaptation for Satellite Images
Fahong Zhang, Yilei Shi, Xiao Xiang Zhu
Domain shift caused by, e.g., different geographical regions or acquisition conditions is a common issue in machine learning for global scale satellite image processing. A promisin…
Few-shot Object Detection in Remote Sensing: Lifting the Curse of Incompletely Annotated Novel Objects
Fahong Zhang, Yilei Shi, Zhitong Xiong +1
Object detection is an essential and fundamental task in computer vision and satellite image processing. Existing deep learning methods have achieved impressive performance thanks…
Exploring Geometric Deep Learning For Precipitation Nowcasting
Shan Zhao, Sudipan Saha, Zhitong Xiong +2
Precipitation nowcasting (up to a few hours) remains a challenge due to the highly complex local interactions that need to be captured accurately. Convolutional Neural Networks rel…
Decoupling Common and Unique Representations for Multimodal Self-supervised Learning
Yi Wang, Conrad M Albrecht, Nassim Ait Ali Braham +3
The increasing availability of multi-sensor data sparks wide interest in multimodal self-supervised learning. However, most existing approaches learn only common representations ac…
High Quality Large-Scale 3-D Urban Mapping with Multi-Master TomoSAR
Yilei Shi, Richard Bamler, Yuanyuan Wang +1
Multi-baseline interferometric synthetic aperture radar (InSAR) techniques are effective approaches for retrieving the 3-D information of urban areas. In order to obtain a plausibl…
Enabling Country-Scale Land Cover Mapping with Meter-Resolution Satellite Imagery
Xin-Yi Tong, Gui-Song Xia, Xiao Xiang Zhu
High-resolution satellite images can provide abundant, detailed spatial information for land cover classification, which is particularly important for studying the complicated buil…
Monitoring Urban Forests from Auto-Generated Segmentation Maps
Conrad M Albrecht, Chenying Liu, Yi Wang +2
We present and evaluate a weakly-supervised methodology to quantify the spatio-temporal distribution of urban forests based on remotely sensed data with close-to-zero human interac…
GLF-CR: SAR-Enhanced Cloud Removal with Global-Local Fusion
Fang Xu, Yilei Shi, Patrick Ebel +4
The challenge of the cloud removal task can be alleviated with the aid of Synthetic Aperture Radar (SAR) images that can penetrate cloud cover. However, the large domain gap betwee…
Self-supervised Vision Transformers for Joint SAR-optical Representation Learning
Yi Wang, Conrad M Albrecht, Xiao Xiang Zhu
Self-supervised learning (SSL) has attracted much interest in remote sensing and earth observation due to its ability to learn task-agnostic representations without human annotatio…
Disentangled Latent Transformer for Interpretable Monocular Height Estimation
Zhitong Xiong, Sining Chen, Yilei Shi +1
Monocular height estimation (MHE) from remote sensing imagery has high potential in generating 3D city models efficiently for a quick response to natural disasters. Most existing w…
Landscape of Neural Architecture Search across sensors: how much do they differ ?
Kalifou René Traoré, Andrés Camero, Xiao Xiang Zhu
With the rapid rise of neural architecture search, the ability to understand its complexity from the perspective of a search algorithm is desirable. Recently, Traoré et al. have pr…
-Net: Superresolving SAR Tomographic Inversion via Deep Learning
Kun Qian, Yuanyuan Wang, Yilei Shi +1
Synthetic aperture radar tomography (TomoSAR) has been extensively employed in 3-D reconstruction in dense urban areas using high-resolution SAR acquisitions. Compressive sensing (…
A Survey of Uncertainty in Deep Neural Networks
Jakob Gawlikowski, Cedrique Rovile Njieutcheu Tassi, Mohsin Ali +11
Due to their increasing spread, confidence in neural network predictions became more and more important. However, basic neural networks do not deliver certainty estimates or suffer…
Multimodal Remote Sensing Benchmark Datasets for Land Cover Classification with A Shared and Specific Feature Learning Model
Danfeng Hong, Jingliang Hu, Jing Yao +2
As remote sensing (RS) data obtained from different sensors become available largely and openly, multimodal data processing and analysis techniques have been garnering increasing i…
A lightweight deep learning based cloud detection method for Sentinel-2A imagery fusing multi-scale spectral and spatial features
Jun Li, Zhaocong Wu, Zhongwen Hu +5
Clouds are a very important factor in the availability of optical remote sensing images. Recently, deep learning-based cloud detection methods have surpassed classical methods base…
Generative modeling of spatio-temporal weather patterns with extreme event conditioning
Konstantin Klemmer, Sudipan Saha, Matthias Kahl +2
Deep generative models are increasingly used to gain insights in the geospatial data domain, e.g., for climate data. However, most existing approaches work with temporal snapshots…
Aerial Scene Understanding in The Wild: Multi-Scene Recognition via Prototype-based Memory Networks
Yuansheng Hua, Lichao Moua, Jianzhe Lin +2
Aerial scene recognition is a fundamental visual task and has attracted an increasing research interest in the last few years. Most of current researches mainly deploy efforts to c…
Towards a Collective Agenda on AI for Earth Science Data Analysis
Devis Tuia, Ribana Roscher, Jan Dirk Wegner +3
In the last years we have witnessed the fields of geosciences and remote sensing and artificial intelligence to become closer. Thanks to both the massive availability of observatio…
Trusting small training dataset for supervised change detection
Sudipan Saha, Biplab Banerjee, Xiao Xiang Zhu
Deep learning (DL) based supervised change detection (CD) models require large labeled training data. Due to the difficulty of collecting labeled multi-temporal data, unsupervised…
Out-of-distribution detection in satellite image classification
Jakob Gawlikowski, Sudipan Saha, Anna Kruspe +1
In satellite image analysis, distributional mismatch between the training and test data may arise due to several reasons, including unseen classes in the test data and differences…
MultiScene: A Large-scale Dataset and Benchmark for Multi-scene Recognition in Single Aerial Images
Yuansheng Hua, Lichao Mou, Pu Jin +1
Aerial scene recognition is a fundamental research problem in interpreting high-resolution aerial imagery. Over the past few years, most studies focus on classifying an image into…