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Found 8 papers, 7 papers with code
Robust Burned Area Delineation through Multitask Learning
In this dataset, we provide annotations for multiple tasks, including burned area delineation and land cover segmentation.
A Multimodal Supervised Machine Learning Approach for Satellite-based Wildfire Identification in Europe
The increasing frequency of catastrophic natural events, such as wildfires, calls for the development of rapid and automated wildfire detection systems.
Land Cover Segmentation with Sparse Annotations from Sentinel-2 Imagery
Land cover (LC) segmentation plays a critical role in various applications, including environmental analysis and natural disaster management.
A Comparative Evaluation of Deep Learning Techniques for Photovoltaic Panel Detection from Aerial Images
Our benchmark evaluation shows that both semantic and instance segmentation techniques can be effective for detecting and mapping PV panels.
Hierarchical Instance Mixing across Domains in Aerial Segmentation
We investigate the task of unsupervised domain adaptation in aerial semantic segmentation and discover that the current state-of-the-art algorithms designed for autonomous driving based on domain mixing do not translate well to the aerial setting.
MMFlood: A Multimodal Dataset for Flood Delineation From Satellite Imagery
To provide baseline performances on the MMFlood test set, we conduct a number of experiments of the flood delineation task using state-of-art deep learning models, and we evaluate the performance gains of entropy-based sampling and multi-encoder architectures, which are respectively used to tackle two of the main challenges posed by MMFlood, namely the class unbalance and the multimodal setting.
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Augmentation Invariance and Adaptive Sampling in Semantic Segmentation of Agricultural Aerial Images
We observe that the existing methods used for this task are designed without considering two characteristics of the aerial data: (i) the top-down perspective implies that the model cannot rely on a fixed semantic structure of the scene, because the same scene may be experienced with different rotations of the sensor; (ii) there can be a strong imbalance in the distribution of semantic classes because the relevant objects of the scene may appear at extremely different scales (e. g., a field of crops and a small vehicle).
A Contrastive Distillation Approach for Incremental Semantic Segmentation in Aerial Images
Incremental learning represents a crucial task in aerial image processing, especially given the limited availability of large-scale annotated datasets.
