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https://github.com/michaelthoreau/SearchAndRescueNet


https://github.com/michaelthoreau/SearchAndRescueNet

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README

        

# Satellite Imagery for Search And Rescue Dataset - [ArXiv](https://arxiv.org/pdf/2107.12469.pdf)

This is a single class dataset consisting of tiles of satellite imagery labeled with potential 'targets'. Labelers were instructed to draw boxes around anything they suspect may a paraglider wing, missing in a remote area of Nevada. Volunteers were shown examples of similar objects already in the environment for comparison. The missing wing, as it was found after 3 weeks, is shown below.

![anomaly](https://michaeltpublic.s3.amazonaws.com/images/anomaly_small.jpg)

The dataset contains the following:

| Set | Images | Annotations |
| ----------- | ----------- | ----------- |
| Train | 1808 | 3048 |
| Validate | 490 | 747 |
| Test | 254 | 411 |
| Total | 2552 |4206 |

The data is in the [COCO format](https://www.immersivelimit.com/tutorials/create-coco-annotations-from-scratch), and is directly compatible with faster r-cnn as implemented in Facebook's [Detectron2](https://github.com/facebookresearch/detectron2).

## Getting hold of the Data

Download the data here: [sarnet.zip](https://michaeltpublic.s3.amazonaws.com/sarnet.zip)

Or follow these steps

```shell
# download the dataset
wget https://michaeltpublic.s3.amazonaws.com/sarnet.zip

# extract the files
unzip sarnet.zip
```

## Getting started
Get started with a Faster R-CNN model pretrained on SaRNet: [SaRNet_Demo.ipynb](https://github.com/michaelthoreau/SearchAndRescueNet/blob/master/SaRNet_Demo.ipynb)

## Source Code for Paper
Source code for the paper is located here: [SaRNet_train_test.ipynb](https://github.com/michaelthoreau/SearchAndRescueNet/blob/master/SaRNet_train_test.ipynb)

## Cite this dataset
```
@misc{thoreau2021sarnet,
title={SaRNet: A Dataset for Deep Learning Assisted Search and Rescue with Satellite Imagery},
author={Michael Thoreau and Frazer Wilson},
year={2021},
eprint={2107.12469},
archivePrefix={arXiv},
primaryClass={eess.IV}
}
```

## Acknowledgment
The source data was generously provided by Planet Labs, Airbus Defence and Space, and Maxar Technologies.