AGENTS.md

Cross-Modal Satellite Image Retrieval

Project Context

A multi-modal satellite image retrieval system that finds semantically similar remote sensing images across different sensor modalities (optical, SAR, multispectral). Users query with an image from one modality and receive ranked results from the same or different modalities.

Core Value: Retrieve semantically similar satellite images across sensor modalities with measurable accuracy (F1@5, F1@10) and acceptable query latency.

Workflow

This project uses GSD (Get Shit Done) workflow. Key commands:

• /gsd-discuss-phase N — Gather context for phase N
• /gsd-plan-phase N — Create detailed plan for phase N
• /gsd-execute-phase N — Execute plans in phase N
• /gsd-verify-work — Validate built features

Tech Stack

• Framework: PyTorch 2.x
• Pre-trained Models: CLOSP / DOFA-CLIP / SARCLIP (HuggingFace)
• Vector Search: FAISS (faiss-cpu)
• UI: Gradio 4.x
• Deployment: HuggingFace Spaces

Key Files

• .planning/PROJECT.md — Project context and goals
• .planning/REQUIREMENTS.md — v1 requirements (27 total)
• .planning/ROADMAP.md — Phase structure (7 phases)
• .planning/config.json — Workflow preferences
• .planning/research/ — Domain research

Phase Overview

Phase	Goal	Requirements
1	Data & Preprocessing	DATA-01 to DATA-04
2	Feature Extraction	FEAT-01 to FEAT-04
3	Retrieval Engine	RETR-01 to RETR-05
4	Same/Cross-Modal Retrieval	SAME-01 to SAME-03, CROSS-01 to CROSS-04
5	Evaluation Metrics	EVAL-01 to EVAL-06
6	Gradio UI	UI-01 to UI-04
7	HuggingFace Deployment	UI-05

Evaluation Metrics

• F1-score@5 (same-modal)
• F1-score@10 (same-modal)
• F1-score@5 (cross-modal)
• F1-score@10 (cross-modal)
• Average retrieval time per query

Notes

• Use pre-trained models, don't train from scratch
• Pre-compute all gallery embeddings (don't extract on-the-fly)
• Per-modality preprocessing is critical (different channel counts)
• Cross-modal retrieval is harder than same-modal — focus there