42 citations · 144 across the 23 of their papers we have counts for
23 papers
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…
AutoLCZ: Towards Automatized Local Climate Zone Mapping from Rule-Based Remote Sensing
Chenying Liu, Hunsoo Song, Anamika Shreevastava +1
Local climate zones (LCZs) established a standard classification system to categorize the landscape universe for improved urban climate studies. Existing LCZ mapping is guided by h…
Climatic & Anthropogenic Hazards to the Nasca World Heritage: Application of Remote Sensing, AI, and Flood Modelling
Masato Sakai, Marcus Freitag, Akihisa Sakurai +2
Preservation of the Nasca geoglyphs at the UNESCO World Heritage Site in Peru is urgent as natural and human impact accelerates. More frequent weather extremes such as flashfloods…
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…
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…
Task Specific Pretraining with Noisy Labels for Remote Sensing Image Segmentation
Chenying Liu, Conrad M Albrecht, Yi Wang +1
Compared to supervised deep learning, self-supervision provides remote sensing a tool to reduce the amount of exact, human-crafted geospatial annotations. While image-level informa…
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…
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…
Deep Semantic Model Fusion for Ancient Agricultural Terrace Detection
Yi Wang, Chenying Liu, Arti Tiwari +4
Discovering ancient agricultural terraces in desert regions is important for the monitoring of long-term climate changes on the Earth's surface. However, traditional ground surveys…
Semi-Supervised Learning for hyperspectral images by non parametrically predicting view assignment
Shivam Pande, Nassim Ait Ali Braham, Yi Wang +3
Hyperspectral image (HSI) classification is gaining a lot of momentum in present time because of high inherent spectral information within the images. However, these images suffer…
DeepLCZChange: A Remote Sensing Deep Learning Model Architecture for Urban Climate Resilience
Wenlu Sun, Yao Sun, Chenying Liu +1
Urban land use structures impact local climate conditions of metropolitan areas. To shed light on the mechanism of local climate wrt. urban land use, we present a novel, data-drive…
Biomass Estimation and Uncertainty Quantification from Tree Height
Qian Song, Conrad M Albrecht, Zhitong Xiong +1
We propose a tree-level biomass estimation model approximating allometric equations by LiDAR data. Since tree crown diameters estimation is challenging from spaceborne LiDAR measur…
SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observation
Yi Wang, Nassim Ait Ali Braham, Zhitong Xiong +3
Self-supervised pre-training bears potential to generate expressive representations without human annotation. Most pre-training in Earth observation (EO) are based on ImageNet or m…
Aboveground carbon biomass estimate with Physics-informed deep network
Juan Nathaniel, Levente J. Klein, Campbell D. Watson +2
The global carbon cycle is a key process to understand how our climate is changing. However, monitoring the dynamics is difficult because a high-resolution robust measurement of ke…
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…
Self-supervised Learning in Remote Sensing: A Review
Yi Wang, Conrad M Albrecht, Nassim Ait Ali Braham +2
In deep learning research, self-supervised learning (SSL) has received great attention triggering interest within both the computer vision and remote sensing communities. While the…
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…
AutoGeoLabel: Automated Label Generation for Geospatial Machine Learning
Conrad M Albrecht, Fernando Marianno, Levente J Klein
A key challenge of supervised learning is the availability of human-labeled data. We evaluate a big data processing pipeline to auto-generate labels for remote sensing data. It is…
Quantification of Carbon Sequestration in Urban Forests
Levente J. Klein, Wang Zhou, Conrad M. Albrecht
Vegetation, trees in particular, sequester carbon by absorbing carbon dioxide from the atmosphere. However, the lack of efficient quantification methods of carbon stored in trees r…
Map Generation from Large Scale Incomplete and Inaccurate Data Labels
Rui Zhang, Conrad Albrecht, Wei Zhang +4
Accurately and globally mapping human infrastructure is an important and challenging task with applications in routing, regulation compliance monitoring, and natural disaster respo…
Learning and Recognizing Archeological Features from LiDAR Data
Conrad M Albrecht, Chris Fisher, Marcus Freitag +4
We present a remote sensing pipeline that processes LiDAR (Light Detection And Ranging) data through machine & deep learning for the application of archeological feature detection…
Event Clustering & Event Series Characterization on Expected Frequency
Conrad M Albrecht, Marcus Freitag, Theodore G van Kessel +2
We present an efficient clustering algorithm applicable to one-dimensional data such as e.g. a series of timestamps. Given an expected frequency , we introduce an $\mathca…