Bylines, paper authorship, correspondence addresses and the founder blurb under
each post named an individual. They name the organisation that published the
work instead: the brand on posts and pages, the research byline on papers, and
research@ in place of a personal address. Both are what these repositories
already used elsewhere — nothing new was coined.
Co-authors are untouched. Where a paper named someone else alongside, they stay
named; only the one byline changed. Bibliography entries, running heads,
PDF metadata and affiliations move with it, so a citation still resolves to
whoever the cited paper names.
Sample and fixture data uses the placeholder name these tests already use
elsewhere, so no assertion changed meaning.
Co-authored-by: Hanzo Dev <[email protected]>
This proposal specifies an AI-powered satellite imagery analysis system for ecological monitoring at global scale. The system processes imagery from Sentinel-2, Landsat, and commercial satellite providers through Zen-VL models (ZIP-0416) to detect deforestation, monitor habitat fragmentation, track water body changes, assess vegetation health, and estimate carbon stock. Results are integrated into the content-addressable knowledge base (ZIP-0404) and used by conservation agents (ZIP-0400) for real-time habitat status reports.
Motivation
Satellite imagery provides the only practical way to monitor ecosystems at global scale. However, manual analysis of satellite data is impossibly slow: a single Sentinel-2 tile covers 100x100 km at 10m resolution, producing 290 million pixels per pass, with global coverage every 5 days. AI is required to process this volume meaningfully.