https://deeplearning.earth/tags/oriented-det/Recent content in Oriented-Det on DeepLearning.EarthHugo -- gohugo.ioen-usTue, 23 Jun 2026 09:00:00 +0700https://deeplearning.earth/posts/2026-06-23_oriented_rcnn_detections_for_the_15_dota_classes/Tue, 23 Jun 2026 09:00:00 +0700https://deeplearning.earth/posts/2026-06-23_oriented_rcnn_detections_for_the_15_dota_classes/Oriented object detection becomes concrete when you look at the boxes. In this post we will walk through detections produced by Oriented R-CNN on the 15 original classes from DOTA v1.0, the benchmark dataset that shaped much of the modern work on rotated object detection in aerial imagery. DOTA matters because it is not a neat toy dataset. Its images contain large scenes, dense object layouts, arbitrary object directions, and strong scale variation.https://deeplearning.earth/posts/2026-06-22_oriented-det_v0_1_0_sovereign_oriented_object_detection_for_eo/Mon, 22 Jun 2026 09:00:00 +0700https://deeplearning.earth/posts/2026-06-22_oriented-det_v0_1_0_sovereign_oriented_object_detection_for_eo/In May I announced that Oriented-det was coming. Today it is here. Oriented-Det v0.1.0 is officially released under the Apache 2.0 license, available on PyPI as oriented-det, and fully documented. This post covers the main ideas behind the framework and how to get started. What is Oriented-det? Link to heading Oriented-det is a lightweight PyTorch library for rotated object detection in aerial and satellite imagery. It targets the workflows where orientation actually matters — ships in a harbor, aircraft on an apron, vehicles in a dense parking lot — and where axis-aligned boxes simply miss the point.https://deeplearning.earth/posts/2026-05-28_introducing_oriented-det_sovereign_oriented_object_detection_for_eo/Thu, 28 May 2026 09:00:00 +0700https://deeplearning.earth/posts/2026-05-28_introducing_oriented-det_sovereign_oriented_object_detection_for_eo/Oriented-det is coming. Link to heading Oriented-det is a new offering for teams who need oriented object detection in Earth Observation (ships, aircrafts, vehicles) with a strong focus on sovereignty, license clarity, and time‑to‑deployment. I’m planning an official release for June 2026. Key selling points Link to heading Sovereign by design Designed to run where you need it: on‑prem, private cloud, regulated environments No “hosted inference” requirement and no platform lock‑in assumptions Open-source with a pragmatic license (Apache 2.