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https://github.com/rodrigoberriel/satellite-crosswalk-classification

Deep Learning Based Large-Scale Automatic Satellite Crosswalk Classification (GRSL, 2017)
https://github.com/rodrigoberriel/satellite-crosswalk-classification

dataset deep-learning remote-sensing satellite-imagery

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Deep Learning Based Large-Scale Automatic Satellite Crosswalk Classification (GRSL, 2017)

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README 

        
# Deep Learning Based Large-Scale Automatic Satellite Crosswalk Classification



[Rodrigo F. Berriel](http://rodrigoberriel.com), André Teixeira Lopes, [Alberto F. de Souza](https://inf.ufes.br/~alberto), and [Thiago Oliveira-Santos](https://www.inf.ufes.br/~todsantos/home)



[IEEE Geoscience and Remote Sensing Letters](http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=8859): [10.1109/LGRS.2017.2719863](https://doi.org/10.1109/LGRS.2017.2719863)



[![Thumbnail](https://github.com/rodrigoberriel/satellite-crosswalk-classification/blob/master/images/thumbnail.jpg)](http://ieeexplore.ieee.org/document/7979607/)



High-resolution satellite imagery have been increasingly used on remote sensing classification problems. One of the main factors is the availability of this kind of data. Even though, very little effort has been placed on the zebra crossing classification problem. In this letter, crowdsourcing systems are exploited in order to enable the automatic acquisition and annotation of a large-scale satellite imagery database for crosswalks related tasks. Then, this dataset is used to train deep-learning-based models in order to accurately classify satellite images that contains or not zebra crossings. A novel dataset with more than 240,000 images from 3 continents, 9 countries and more than 20 cities were used in the experiments. Experimental results showed that freely available crowdsourcing data can be used to accurately (96.78%) train robust models to perform crosswalk classification on a global scale.



---



### Dataset Automatic Acquisition and Annotation

To download the dataset, you should run the command below for each region of interest. Be careful with your API quota.



```bash

python crosswalk-downloader.py --region={REGION_NAME} --negative --positive --key={API_KEY}

# e.g. to download the crosswalks only of the regions in Asia

python crosswalk-downloader.py --region=asia --positive --key={API_KEY}

```



### Test with your data

Pre-trained models are available [here](https://github.com/rodrigoberriel/satellite-crosswalk-classification/blob/master/models/).



This [Python notebook](https://github.com/rodrigoberriel/satellite-crosswalk-classification/blob/master/inference/) may help you with the inference process.



### Dataset

The dataset used in this work is defined by a group of city-based regions. As stated in the paper, "even though each part of the dataset is named after a city, some selected regions were large enough to partially include neighboring towns". The regions can be seen in the file `regions.json` and a summary of the dataset can be seen below.



| Dataset Name               | Crosswalks | No-Crosswalks |

|----------------------------|------------|---------------|

| Europe-Belgium-Brussels    | 7,916      | 18,739        |

| Europe-France-Lion         | 5,168      | 11,960        |

| Europe-France-Paris        | 5,828      | 13,353        |

| Europe-France-Marseille    | 2,615      | 6,668         |

| Europe-France-Toulouse     | 4,794      | 11,046        |

| Europe-Italy-Turim         | 5,081      | 11,324        |

| Europe-Italy-Milan         | 4,536      | 10,147        |

| Europe-Portugal-Porto      | 1,630      | 3,786         |

| Europe-Portugal-Lisbon     | 1,731      | 4,460         |

| Europe-Spain-Saragoca      | 1,413      | 3,310         |

| Europe-Switzerland-Zurich  | 1,842      | 4,668         |

| **Europe**                 | **42,554** | **99,461**    |

| America-USA-Seattle        | 1,276      | 2,929         |

| America-USA-WashingtonDC   | 2,838      | 6,503         |

| America-USA-Philadelphia   | 2,356      | 6,145         |

| America-USA-NewYork        | 2,191      | 4,919         |

| America-Canada-Mississauga | 3,259      | 7,463         |

| America-Canada-Toronto     | 3,902      | 8,852         |

| **America**                | **15,822** | **36,811**    |

| Asia-Japan-Tokyo           | 6,888      | 15,529        |

| Asia-Japan-Toyokawa        | 1,837      | 4,140         |

| Asia-Japan-Sapporo         | 6,946      | 15,780        |

| **Asia**                   | **15,671** | **35,449**    |

| **Total**                  | **74,047** | **171,721**   |



### Positive Samples

![PositiveSamples](https://github.com/rodrigoberriel/satellite-crosswalk-classification/blob/master/images/positive-samples.png)



### Negative Samples

![NegativeSamples](https://github.com/rodrigoberriel/satellite-crosswalk-classification/blob/master/images/negative-samples.png)



#### BibTeX



    @article{berriel2017grsl,

        Author  = {Rodrigo F. Berriel and Andre T. Lopes and Alberto F. de Souza and Thiago Oliveira-Santos},

        Title   = {{Deep Learning Based Large-Scale Automatic Satellite Crosswalk Classification}},

        Journal = {IEEE Geoscience and Remote Sensing Letters},

        Year    = {2017},

        DOI     = {10.1109/LGRS.2017.2719863},

        ISSN    = {1545-598X},

    }