67 citations · 148 across the 18 of their papers we have counts for
18 papers
Deep Evidential Regression for Sparse Forest Height Estimation from Multimodal Satellite Imagery
Laura Bader, Muhammad Ammar Ahmed, Xiao Xiang Zhu +1
Accurate estimation of forest height from satellite imagery is essential for applications such as carbon accounting, biodiversity monitoring, and ecosystem management. While recent…
EO-VAE: Towards A Multi-sensor Tokenizer for Earth Observation Data
Nils Lehmann, Yi Wang, Zhitong Xiong +1
State-of-the-art generative image and video models rely heavily on tokenizers that compress high-dimensional inputs into more efficient latent representations. While this paradigm…
Reconstructing Building Height from Spaceborne TomoSAR Point Clouds Using a Dual-Topology Network
Zhaiyu Chen, Yuanyuan Wang, Yilei Shi +1
Reliable building height estimation is essential for various urban applications. Spaceborne SAR tomography (TomoSAR) provides weather-independent, side-looking observations that ca…
Towards Unified Vision Language Models for Forest Ecological Analysis in Earth Observation
Xizhe Xue, Xiao Xiang Zhu
Recent progress in vision language models (VLMs) has enabled remarkable perception and reasoning capabilities, yet their potential for scientific regression in Earth Observation (E…
Generating Synthetic Oracle Datasets to Analyze Noise Impact: A Study on Building Function Classification Using Tweets
Shanshan Bai, Anna Kruspe, Xiaoxiang Zhu
Tweets provides valuable semantic context for earth observation tasks and serves as a complementary modality to remote sensing imagery. In building function classification (BFC), t…
i-WiViG: Interpretable Window Vision GNN
Ivica Obadic, Dmitry Kangin, Adrian Höhl +3
Vision graph neural networks have emerged as a popular approach for modeling the global and spatial context for image recognition. However, a significant drawback of these methods…
SpectralEarth: Training Hyperspectral Foundation Models at Scale
Nassim Ait Ali Braham, Conrad M Albrecht, Julien Mairal +3
Foundation models have triggered a paradigm shift in computer vision and are increasingly being adopted in remote sensing, particularly for multispectral imagery. Yet, their potent…
Multi-Label Guided Soft Contrastive Learning for Efficient Earth Observation Pretraining
Yi Wang, Conrad M Albrecht, Xiao Xiang Zhu
Self-supervised pretraining on large-scale satellite data has raised great interest in building Earth observation (EO) foundation models. However, many important resources beyond p…
CromSS: Cross-modal pre-training with noisy labels for remote sensing image segmentation
Chenying Liu, Conrad Albrecht, Yi Wang +1
We explore the potential of large-scale noisily labeled data to enhance feature learning by pretraining semantic segmentation models within a multi-modal framework for geospatial a…
Contrastive Pretraining for Visual Concept Explanations of Socioeconomic Outcomes
Ivica Obadic, Alex Levering, Lars Pennig +3
Predicting socioeconomic indicators from satellite imagery with deep learning has become an increasingly popular research direction. Post-hoc concept-based explanations can be an i…
AIO2: Online Correction of Object Labels for Deep Learning with Incomplete Annotation in Remote Sensing Image Segmentation
Chenying Liu, Conrad M Albrecht, Yi Wang +2
While the volume of remote sensing data is increasing daily, deep learning in Earth Observation faces lack of accurate annotations for supervised optimization. Crowdsourcing projec…
PixelDINO: Semi-Supervised Semantic Segmentation for Detecting Permafrost Disturbances
Konrad Heidler, Ingmar Nitze, Guido Grosse +1
Arctic Permafrost is facing significant changes due to global climate change. As these regions are largely inaccessible, remote sensing plays a crucial rule in better understanding…
Feature Guided Masked Autoencoder for Self-supervised Learning in Remote Sensing
Yi Wang, Hugo Hernández Hernández, Conrad M Albrecht +1
Self-supervised learning guided by masked image modelling, such as Masked AutoEncoder (MAE), has attracted wide attention for pretraining vision transformers in remote sensing. How…
UCDFormer: Unsupervised Change Detection Using a Transformer-driven Image Translation
Qingsong Xu, Yilei Shi, Jianhua Guo +2
Change detection (CD) by comparing two bi-temporal images is a crucial task in remote sensing. With the advantages of requiring no cumbersome labeled change information, unsupervis…
A Deep Active Contour Model for Delineating Glacier Calving Fronts
Konrad Heidler, Lichao Mou, Erik Loebel +3
Choosing how to encode a real-world problem as a machine learning task is an important design decision in machine learning. The task of glacier calving front modeling has often bee…
RRSIS: Referring Remote Sensing Image Segmentation
Zhenghang Yuan, Lichao Mou, Yuansheng Hua +1
Localizing desired objects from remote sensing images is of great use in practical applications. Referring image segmentation, which aims at segmenting out the objects to which a g…
Artificial intelligence to advance Earth observation: : A review of models, recent trends, and pathways forward
Devis Tuia, Konrad Schindler, Begüm Demir +11
Earth observation (EO) is a prime instrument for monitoring land and ocean processes, studying the dynamics at work, and taking the pulse of our planet. This article gives a bird's…
Multi-Modal and Multi-Resolution Data Fusion for High-Resolution Cloud Removal: A Novel Baseline and Benchmark
Fang Xu, Yilei Shi, Patrick Ebel +2
Cloud removal is a significant and challenging problem in remote sensing, and in recent years, there have been notable advancements in this area. However, two major issues remain h…