Writing
Technical explanations, release notes, and field reports from building AI systems.
Inside FOTBCD-CC: analysing 321,200 aerial captions
An analysis of 321,200 model-generated aerial captions: dataset composition, descriptions per location, language patterns, and candidates for visual review.
Distilling Qwen3.8-Max into a smaller aerial change model
The decisions behind making aerial change captioning affordable: teacher supervision, prompt caching, LoRA adaptation, and quality evaluation.
Training a multimodal model to retrieve aerial change
The design decisions behind teaching DINOv3 and Qwen to connect descriptions of change with before-and-after aerial imagery.
Why Binary Change Detection Isn't Enough
A practical look at what disappears when every temporal difference is reduced to changed or unchanged.
Zero-Shot Damage Assessment: Evaluating MBCTD on the 2025 LA Wildfires
Testing whether a building-change model trained on ordinary urban development can identify wildfire destruction in unseen Los Angeles imagery.