Deep learning methods for feature extraction using computer vision are giving concrete results. This talk provides an overview of feature detection from satellite imagery and how machine learning can provide a seamless experience for mappers, allowing them to utilize their knowledge to enrich maps further.
When using OpenStreetMap for disaster response and humanitarian action, time and data quality are critical. Automated tools around OpenStreetMap have revolutionized disaster response, allowing mappers to leverage their local knowledge to organize themselves in action and to contribute to highly relevant maps. Over the last year, Tasking Manager team at HOT conceptualized an initiative to test using data derived using machine learning models to improve task creation, quality of edits and overall experience for mappers.
Within Tasking Manager Working Groups we are collaborating between different actors to test concepts directly applied to two countries: Tanzania and Uganda. Two tools in pilot phase to assist mappers are around 1) Task Creation and facilitation: identify areas that need most work and/ or need an experienced mapper based on task complexity and gap analysis 2) Assisted Mapping: leverage machines to do the heavy lifting around digitizing features, such as buildings and roads, one by one and allowing mappers to focus on their essential craft of mapping.
In this talk the presenters will share their findings, learning and existing challenges with the technology.