Deep Learning is revolutionizing the computer vision field in many industries, and now it is the turn for Earth Observation (EO). A lot of interesting applications can take advantage of satellite imagery, such as traffic management, construction site monitoring or environment protection. Working with EO imagery comes with additional challenges when compared with traditional Machine Learning (ML) vision problems. Nevertheless, they all have something in common, and that is the need of labelled datasets. Different datasets exist today for training object detection algorithms on common images (COCO, Pascal VOC) but that is not the case for EO. In this context, the POINTOUT project (developed by Starlab Barcelona S.L and funded by ESA) is intended to provide an open, collaborative web platform where ML/EO professionals, students and enthusiasts can explore and annotate objects directly on a map in order to build datasets for object detection tasks.


First steps

Disclaimer: The platform is in its first stages of development, so keep in mind this is an on-going work with limited functionality.

In order to access the platform, please go to targetdetection.com. You will see the landing page of the project with some information and contact options. Please get in touch if you want to know more about the project !

Once you go to the app the first thing that you will see is an interactive world-wide map where you can navigate to your area of interest. In order to explore annotations by other users or start making your own, you will need to register. Then, you will be able to access logging with your username and password.

To start using the platform you will have to Register.

Create a new label and add some annotations

You can create a new label using the panel on the right-top corner. Just start typing the name of the label and click on the add button.

Create a new label.

To generate new labels, click on the draw toolbox on the left-bottom corner and start drawing boxes around the objects you want to annotate.

Draw boxes around objects.

Once you are happy with your annotations, click on the save button in order to generate and image corresponding to the current viewport along with the annotations.

Save your annotations.

Explore and rate annotations by other users

When you click on the label, you can see directly on the map annotations made by you (colored background) and other users (transparent background). You can also rate the annotations, which is reflected in the color of the box (red for not rated, yellow for more than one positive rating and green for three or more positive ratings).

Explore and rate annotations.

Download datasets

Finally, you can download a dataset clicking on the download button. A filtering option can be used to download only your annotations, restrict the dataset to an area of interest, etc.

Download a dataset.

Once you download a dataset, you can use it to train your own object detection algorithms using your favorite neural networks framework.

Get in touch !

If you are:

  • An ML/EO professional, student or enthusiast interested in providing feedback to improve the platform.
  • A business developer interested in a platform like Pointout tailored for your use cases.

Contact us at pointoutcommunication@starlab.es.


Follow us on Twitter @Pointoutproject !

If you are attending the Living Planet Symposium in Milan from May 13th to 17th, please meet us at the poster session on May 16th !

Source: Artificial Intelligence on Medium