Abstract
Abstract. Satellite imagery from earth observation missions enable processing big data to gather information about the world. Automatizing the creation of maps that reflect ground truth is a desirable outcome that would aid decision makers to take adequate actions in alignment with the United Nations Sustainable Development Goals. In order to harness the power that the availability of the new generation of satellites enable, it is necessary to implement techniques capable of handling annotations for the massive volume and variability of high spatial resolution imagery for further processing. However, the availability of public datasets for training machine learning models for image segmentation plays an important role for scalability.This work focuses on bridging remote sensing and computer vision by providing an open source based pipeline for generating machine learning training datasets for road detection in an area of interest. The proposed pipeline addresses road detection as a binary classification problem using road annotations existing in OpenStreetMap for creating masks. For this case study, Planet images of 3m resolution are used for creating a training dataset for road detection in Kenya.
Highlights
The overall growth of generated data has propelled the renaissance of Artificial Intelligence (AI) for new approaches to solve problems in different areas
Using satellite imagery for automatic road detection is of utmost importance for map-making; access to high resolution images, pre-processing and the lack of annotated masks ready to use in Machine Learning models (ML) play an important role for scalability. (Demir et al, 2018) state that “satellite images are only recently gaining attention from the [computer vision] community for map composition”
The challenge provides the dataset ready to use; this behavior often present in challenges, inevitably biases the creation of the models to work with the provided images; the models will learn patterns of the cities depicted in the training dataset, neglecting other areas of the world that were not included, specially those with a significantly different landscape
Summary
The overall growth of generated data has propelled the renaissance of Artificial Intelligence (AI) for new approaches to solve problems in different areas. Using satellite imagery for automatic road detection is of utmost importance for map-making; access to high resolution images, pre-processing and the lack of annotated masks ready to use in Machine Learning models (ML) play an important role for scalability. In (Demir et al, 2018) is presented the DeepGlobe 2018 challenge, urging competitors for ML models to “parse the earth through satellite images”, identifying roads among other features. The proposed pipeline is built with pyQGIS, using the Overpass API to consume OpenStreetMap (OSM) data for an area of interest. This process may produce masks with some inaccuracy as opposed to human labelling, but with the possibility of generating a high volume of training data. Even if road extraction is not perfect due to challenging surroundings in the images, like dry rivers that can be confused with unpaved roads, the identification of roads through ML serves to direct a human mapper focus on specific areas in the imagery to map
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