In part I of this post we learnt how to download and process a dataset from POINTOUT. Now, we are going to see how to implement and train an SSD object detecion network with our data.

If you want to know more about POINTOUT check out our previous post.

You can follow this post with the Jupyter notebooks in this repository.

Loading the data

First, we have to load the data generated in part I. We generated several csv files containing the name of the images and their annotations, one containing patches for training and another for evaluation. …


This is the first part of a two part project consisting on training and object detection Deep Learning model using a dataset downloaded from the POINTOUT platform: https://targetdetection.com/. In this first part we are going to learn how to download a dataset and transform it to a suitable format for training a model in part II.

If you want to know more about POINTOUT check out our previous post.

You can follow this post with the Jupyter notebooks in this repository.

Downloading a dataset

The first thing we have to do is go to POINTOUT and download a dataset. …


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…

Juan B. Pedro

Data Scientist working on Deep Learning applied to Satellite Imagery at Starlab Barcelona.

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