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Found 15 papers, 4 papers with code
Evolution of urban areas and land surface temperature
With the global population on the rise, our cities have been expanding to accommodate the growing number of people.
Exploring Geometric Deep Learning For Precipitation Nowcasting
Motivated by this, we explore a geometric deep learning-based temporal Graph Convolutional Network (GCN) for precipitation nowcasting.
Deep Unsupervised Learning for 3D ALS Point Cloud Change Detection
To circumnavigate this dependence, we propose an unsupervised 3D point cloud change detection method mainly based on self-supervised learning using deep clustering and contrastive learning.
Understanding the Role of Weather Data for Earth Surface Forecasting using a ConvLSTM-based Model
Climate change is perhaps the biggest single threat to humankind and the environment, as it severely impacts our terrestrial surface, home to most of the living species.
Ranked #2 on
Earth Surface Forecasting
on EarthNet2021 OOD Track
Spatial Context Awareness for Unsupervised Change Detection in Optical Satellite Images
It uses this model to analyze differences in the pixel and its spatial context-based predictions in subsequent time periods for change detection.
Reiterative Domain Aware Multi-Target Adaptation
Multi-target domain adaptation is a powerful extension in which a single classifier is learned for multiple unlabeled target domains.
Segmentation of VHR EO Images using Unsupervised Learning
The proposed method exploits this property to sample smaller patches from the larger scene and uses deep clustering and contrastive learning to refine the weights of a lightweight deep model composed of a series of the convolution layers along with an embedded channel attention.
Generative modeling of spatio-temporal weather patterns with extreme event conditioning
Deep generative models are increasingly used to gain insights in the geospatial data domain, e. g., for climate data.
Out-of-distribution detection in satellite image classification
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 in the geographic area.
Trusting small training dataset for supervised change detection
Deep learning (DL) based supervised change detection (CD) models require large labeled training data.
Deep Reinforcement Learning for Band Selection in Hyperspectral Image Classification
To this end, we frame the problem of unsupervised band selection as a Markov decision process, propose an effective method to parameterize it, and finally solve the problem by deep reinforcement learning.
Self-Supervised Multisensor Change Detection
In particular, we are interested in the combination of the images acquired by optical and Synthetic Aperture Radar (SAR) sensors.
Towards Bridging the gap between Empirical and Certified Robustness against Adversarial Examples
In this paper, we propose a novel method, called \emph{Certification through Adaptation}, that transforms an AT model into a randomized smoothing classifier during inference to provide certified robustness for $\ell_2$ norm without affecting their empirical robustness against adversarial attacks.
Ultrasound Image Classification using ACGAN with Small Training Dataset
The lack of large labeled data is a bottleneck for the use of deep learning in ultrasound image analysis.
Federated Transfer Learning: concept and applications
This is an hindrance to the further development of AI.
