Türk Uzaktan Algılama ve CBS Dergisi

Yazarlar: Ekrem SARALIOĞLU, Oğuz GÜNGÖR

Konular:Uzaktan Algılama

Anahtar Kelimeler:GIS,Remote sensing,Spatial analysis,Crowdsourcing

Özet: Compared to conventional RGB images, new problems arise in the use of satellite images for deep learning. The absence of labeled training data for remotely sensed images is one of these problems. One of the methods that can solve the problem of insufficient labelled data available for satellite images in a short time and accurately is crowdsourcing. This study introduces a web platform created to ensure that the labelled data for high-resolution satellite images can be collected by the masses. This platform has a dynamic structure and is designed to be used by different users simultaneously. In order to create tagged data, users can use Google Earth satellite images covering the entire Earth’s surface as well as new images added to the database. In this way, it will be possible to generate labelled data for all types of classes (buildings, roads, forests, streams, hazelnuts, tea, ships, planes, etc.) that can be extracted from images around the world. Help documents have been added to the web platform to identify training classes for users, enabling them to use the platform effectively. Users can use the polygon tool to create descriptive fields for the classes specified in the help document to create labels. Data verification module has also been added to the web platform in order to determine the correct and incorrect labels. In this module, users verify and score the labels created by other people using the help document. As a result, the correctly labelled data with the highest score are selected.

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