# geo-deep-learning

Guides neural methods for geospatial imagery, from choosing a model to checking mapped predictions. Useful for segmentation, object detection, and spatial validation.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · ★ 35
- Author: Muhammed Enes Duran
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/muend/geoai-skills/tree/HEAD/skills/geo-deep-learning/SKILL.md
- Updated: 2026-10-17
- HTML: https://affpapa.org/en/nevibecode/skills/geo-deep-learning

## What it does

The skill frames the task before suggesting an architecture: it considers label volume and geographic spread, class balance, and where predictions will be used. It also calls for a simple baseline and geographic or scene-based validation splits. Randomly splitting nearby chips can inflate scores.

Its guidance covers making chips while retaining georeferencing, handling class imbalance during training, and running overlapping windows across large scenes before stitching predictions. It also describes checking error maps, geographic transfer, and alignment with the original imagery. For imbalanced segmentation, it recommends comparing Dice and cross-entropy; Focal loss is not treated as an automatic win.

The workflow is for neural methods on geospatial imagery. For non-neural approaches it points to `remote-sensing-analysis`; when the deliverable is temporal change, it points to `change-detection`.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add muend/geoai-skills --skill geo-deep-learning -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/geo-deep-learning/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/geo-deep-learning/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add muend/geoai-skills --skill geo-deep-learning -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/geo-deep-learning/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add muend/geoai-skills --skill geo-deep-learning -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/geo-deep-learning/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install muend/geoai-skills geo-deep-learning
```

2. **Manually: put the files in place**
- File: `.github/skills/geo-deep-learning/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add muend/geoai-skills --skill geo-deep-learning -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/muend/geoai-skills --path skills/geo-deep-learning
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/geo-deep-learning/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add muend/geoai-skills --skill geo-deep-learning -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/muend/geoai-skills/tree/HEAD/skills/geo-deep-learning/SKILL.md

## When to use it
- choose an architecture based on labels and deployment geography
- split chips by scene without spatial leakage
- inspect class-level scores and error maps
- stitch large-scene predictions while retaining georeferencing

## Example prompts
- Help choose a model for building segmentation, taking the label geography into account.
- Check whether training and validation chips overlap geographically.
- Plan overlapping-window inference for a large scene and verify the output alignment.

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: fe817055eb065bb4.

BEGIN THIRD-PARTY FILE fe817055eb065bb4
```text
---
name: geo-deep-learning
description: >-
  Invoke before recommending, training, or auditing a neural method for
  geospatial imagery, including vision transformers, U-Net/DeepLab/SegFormer,
  object detection, pixel classification, building/road extraction, and EO
  foundation-model fine-tuning. Also invoke for neural chip-split validity,
  IoU/accuracy claims, augmentation, imbalanced losses, spatial validation,
  or sliding-window inference. Use remote-sensing-analysis for non-neural
  methods and change-detection when temporal change is the deliverable.
license: MIT
metadata:
  author: Muhammed Enes Duran
---

# Geospatial Deep Learning

Purpose: deep learning on Earth observation with the two failure modes that
dominate this field designed out from the start: **spatial leakage**
(inflated metrics from nearby train/test pixels) and **georeferencing loss**
(predictions that no longer align with the map).

## Characterise the label set before naming an architecture

Architecture advice given without knowing the label set is guesswork. Before
recommending U-Net versus a foundation model versus a non-deep baseline, state
or ask for:

- **Label count and labelled area** — polygons alone say nothing; 40 polygons
  covering 2 ha and 40 covering 2 000 km² are different problems.
- **Geographic spread** — are the labels clustered in one scene, one season and
  one sensor, or distributed across the deployment domain? Clustered labels cap
  what any model can generalise to, and they decide whether a geographically
  independent validation split is even constructible.
- **Class balance and minority-class pixel fraction**, so loss and sampling
  choices are grounded rather than assumed.
- **Deployment geography** — where predictions will be made, relative to where
  the labels are.

Do not answer "fine-tune a large model or use a simpler approach" before these
are known. When the user has not supplied them, ask and give the provisional
recommendation *conditioned on* the answers ("if the 40 polygons sit in one
scene, then …; if they span the region, then …"), never a single unconditional
recommendation.

## Problem framing first

| Task | Head/architecture default | Metric |
|---|---|---|
| Pixel-wise classes (land cover) | U-Net / DeepLabv3+ (pretrained encoder) | mIoU, per-class IoU |
| Binary extraction (buildings, water, roads) | U-Net + Dice/CE hybrid | IoU, F1; boundary F1 for roads |
| Object detection (vehicles, ships, trees) | YOLO-family / Faster R-CNN, rotated boxes if oriented | mAP@50 |
| Scene classification | Fine-tuned CNN/ViT | F1 (macro) |
| Regression (height, biomass, density) | U-Net with regression head | RMSE/MAE + spatial residual map |

Before any deep model: run a cheap baseline (random forest on bands+indices,
```
END THIRD-PARTY FILE fe817055eb065bb4

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