Deep Learning
for Earth Observation

DL4EO is the editor of oriented-det, an open-source package for oriented object detection on satellite imagery — ships, aircraft, vehicles, and more. Oriented bounding boxes are the core representation, not a side feature: they capture heading, footprint, and dense-scene geometry that axis-aligned detectors miss. The framework targets practical workflows from training and evaluation to inference on large EO imagery.

Oriented-det is released under the Apache license: auditable, forkable, and free of platform lock-in. Teams can run it on-prem, in a private cloud, or in regulated environments — with no hosted-inference requirement. License clarity matters for institutional and commercial programmes where supply-chain control and long-lived maintainability are non-negotiable. You own the stack, the data path, and the deployment surface.

Technology alone does not deliver oriented detection in production — teams need reproducible metrics, sound tiling strategies, and honest evaluation on real EO data. DL4EO publishes benchmarks, documents edge cases, and shares lessons learned so that adoption is built on trust, not black boxes. Training, mentoring, and community engagement remain central to turning open-source tooling into deployable capability.

Hands-on sessions for teams adopting oriented object detection: install and configure oriented-det, prepare EO datasets with proper tiling and annotation, train and evaluate models, and interpret oriented metrics. Formats range from half-day intros to multi-day programmes tailored to your imagery, classes, and deployment constraints.

Expert guidance when you need to move from experiment to production: architecture choices, dataset design without leakage, oriented evaluation, post-processing for dense scenes, and packaging for on-prem or private-cloud deployment. Ongoing support helps your team maintain velocity without rebuilding the stack from scratch.

End-to-end projects to deliver a sovereign oriented detector for your use case — from scoping and annotation strategy through model training, qualification, and deployment. Typical engagements run 3 to 6 months and produce reproducible pipelines, documented metrics, and production-ready containers your team can operate independently.