Train Custom Deep Learning Models Without Coding using QGIS, Roboflow and Ultralytics

Train Custom Deep Learning Models Without Coding using QGIS, Roboflow and Ultralytics https://videos.qwast-gis.com/videos/watch/e460d7e6-5a4b-4ac5-8d4c-863e34fcd31c Want to bring AI-powered object detection into your geospatial workflows without writing a single line of code? In this video, you’ll learn how to train your own custom deep learning model and use it directly inside the QGIS. You'll first see how training tiles are prepared in with the Deepness plugin in QGIS. Next, the training dataset gets annotated in Roboflow, followed by training a YOLO model for object detection in Ultralytics. Finally, the model is applied to detect wind turbines in aerial photography. Data: Beeldmaterial QGIS version: 3.44 Plugin: Deepness Roboflow Ultalytics Thanks to Peter Schols from Geo-ICT to provide some clues. 0:00 Introduction 0:49 Export training data 2:08 Annotate data in Roboflow 5:47 Export training, validation and test data to PyTorch format 6:43 Training a deep learning model in Ultralytics 9:09 Apply deep learning model to aerial photograph in QGIS Sun, 05 Apr 2026 23:27:46 GMT https://validator.w3.org/feed/docs/rss2.html PeerTube - https://videos.qwast-gis.com Train Custom Deep Learning Models Without Coding using QGIS, Roboflow and Ultralytics https://videos.qwast-gis.com/lazy-static/avatars/b67f5a64-f395-4e3b-beec-2ed3d3c795b7.png https://videos.qwast-gis.com/videos/watch/e460d7e6-5a4b-4ac5-8d4c-863e34fcd31c All rights reserved, unless otherwise specified in the terms specified at https://videos.qwast-gis.com/about and potential licenses granted by each content's rightholder.