Disaster Response
27 papers with code • 1 benchmarks • 7 datasets
fMoW
xView
xBD
RoadTracer
CrisisMMD
MSAW
MEDIC
Most implemented papers
The Multi-Temporal Urban Development SpaceNet Dataset
Each building is assigned a unique identifier (i. e. address), which permits tracking of individual objects over time.
Flood Segmentation on Sentinel-1 SAR Imagery with Semi-Supervised Learning
sidgan/ETCI-2021-Competition-on-Flood-Detection
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NeurIPS Workshop AI4Scien 2021
Floods wreak havoc throughout the world, causing billions of dollars in damages, and uprooting communities, ecosystems and economies.
Human operator cognitive availability aware Mixed-Initiative control
The controller leverages a state-of-the-art computer vision method and an off-the-shelf web camera to infer the cognitive availability of the operator and inform the AI-initiated LOA switching.
MEDIC: A Multi-Task Learning Dataset for Disaster Image Classification
firojalam/medic
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This is the first dataset of its kind: social media images, disaster response, and multi-task learning research.
A Deep Learning Ensemble Framework for Off-Nadir Geocentric Pose Prediction
Then, the elevation masks are concatenated with the RGB images to form four-channel inputs fed into a second convolutional model, which predicts orientation angle and magnification scale.
Continual VQA for Disaster Response Systems
Thus, we instead use pre-trained embeddings of text and image from this model for our supervised training and surpass previous state-of-the-art results on the FloodNet dataset.
Unsupervised Wildfire Change Detection based on Contrastive Learning
spaceml-org/fireclr-wildfires
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The aim of this study is to develop an autonomous system built on top of high-resolution multispectral satellite imagery, with an advanced deep learning method for detecting burned area change.
Deep Metric Learning for Unsupervised Remote Sensing Change Detection
wgcban/metric-cd
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This loss is motivated by the principle of metric learning where we simultaneously maximize the distance between change pair-wise pixels while minimizing the distance between no-change pair-wise pixels in bi-temporal image domain and their deep feature domain.
Signal Novelty Detection as an Intrinsic Reward for Robotics
In advanced robot control, reinforcement learning is a common technique used to transform sensor data into signals for actuators, based on feedback from the robot’s environment.
THRawS: A Novel Dataset for Thermal Hotspots Detection in Raw Sentinel-2 Data
Nevertheless, given the growing interest to apply Artificial Intelligence (AI) onboard satellites for time-critical applications, such as natural disaster response, providing raw satellite images could be useful to foster the research on energy-efficient pre-processing algorithms and AI models for onboard-satellite applications.
