21 citations · 46 across the 18 of their papers we have counts for
20 papers
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…
Hierarchical Semi-Supervised Active Learning for Remote Sensing
Wei Huang, Zhitong Xiong, Chenying Liu +1
The performance of deep learning models in remote sensing (RS) strongly depends on the availability of high-quality labeled data. However, collecting large-scale annotations is cos…
ExEBench: Benchmarking Foundation Models on Extreme Earth Events
Shan Zhao, Zhitong Xiong, Jie Zhao +1
Our planet is facing increasingly frequent extreme events, which pose major risks to human lives and ecosystems. Recent advances in machine learning (ML), especially with foundatio…
REOBench: Benchmarking Robustness of Earth Observation Foundation Models
Xiang Li, Yong Tao, Siyuan Zhang +7
Earth observation foundation models have shown strong generalization across multiple Earth observation tasks, but their robustness under real-world perturbations remains underexplo…
GlobalGeoTree: A Multi-Granular Vision-Language Dataset for Global Tree Species Classification
Yang Mu, Zhitong Xiong, Yi Wang +5
Global tree species mapping using remote sensing data is vital for biodiversity monitoring, forest management, and ecological research. However, progress in this field has been con…
Towards a Unified Copernicus Foundation Model for Earth Vision
Yi Wang, Zhitong Xiong, Chenying Liu +8
Advances in Earth observation (EO) foundation models have unlocked the potential of big satellite data to learn generic representations from space, benefiting a wide range of downs…
Panopticon: Advancing Any-Sensor Foundation Models for Earth Observation
Leonard Waldmann, Ando Shah, Yi Wang +6
Earth observation (EO) data features diverse sensing platforms with varying spectral bands, spatial resolutions, and sensing modalities. While most prior work has constrained input…
DOFA-CLIP: Multimodal Vision-Language Foundation Models for Earth Observation
Zhitong Xiong, Yi Wang, Weikang Yu +7
Earth observation (EO) spans a broad spectrum of modalities, including optical, radar, multispectral, and hyperspectral data, each capturing distinct environmental signals. However…
Beyond Grid Data: Exploring Graph Neural Networks for Earth Observation
Shan Zhao, Zhaiyu Chen, Zhitong Xiong +3
Earth Observation (EO) data analysis has been significantly revolutionized by deep learning (DL), with applications typically limited to grid-like data structures. Graph Neural Net…
UrbanSARFloods: Sentinel-1 SLC-Based Benchmark Dataset for Urban and Open-Area Flood Mapping
Jie Zhao, Zhitong Xiong, Xiao Xiang Zhu
Due to its cloud-penetrating capability and independence from solar illumination, satellite Synthetic Aperture Radar (SAR) is the preferred data source for large-scale flood mappin…
On the Foundations of Earth and Climate Foundation Models
Xiao Xiang Zhu, Zhitong Xiong, Yi Wang +7
Foundation models have enormous potential in advancing Earth and climate sciences, however, current approaches may not be optimal as they focus on a few basic features of a desirab…
Causal Graph Neural Networks for Wildfire Danger Prediction
Shan Zhao, Ioannis Prapas, Ilektra Karasante +4
Wildfire forecasting is notoriously hard due to the complex interplay of different factors such as weather conditions, vegetation types and human activities. Deep learning models s…
Neural Plasticity-Inspired Multimodal Foundation Model for Earth Observation
Zhitong Xiong, Yi Wang, Fahong Zhang +7
Earth observation (EO) in open-world settings presents a unique challenge: different applications rely on diverse sensor modalities, each with varying ground sampling distances, sp…
ChatEarthNet: A Global-Scale Image-Text Dataset Empowering Vision-Language Geo-Foundation Models
Zhenghang Yuan, Zhitong Xiong, Lichao Mou +1
An in-depth comprehension of global land cover is essential in Earth observation, forming the foundation for a multitude of applications. Although remote sensing technology has adv…
Efficient Subseasonal Weather Forecast using Teleconnection-informed Transformers
Shan Zhao, Zhitong Xiong, Xiao Xiang Zhu
Subseasonal forecasting, which is pivotal for agriculture, water resource management, and early warning of disasters, faces challenges due to the chaotic nature of the atmosphere.…
One for All: Toward Unified Foundation Models for Earth Vision
Zhitong Xiong, Yi Wang, Fahong Zhang +1
Foundation models characterized by extensive parameters and trained on large-scale datasets have demonstrated remarkable efficacy across various downstream tasks for remote sensing…
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…
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…