Dev
F 46 completed
Ai Ml
unknown / r · tiny
6
Files
1,711
LOC
0
Frameworks
2
Languages
Pipeline State
completedRun ID
#370854Phase
doneProgress
1%Started
Finished
2026-04-13 01:31:02LLM tokens
0Pipeline Metadata
Stage
CatalogedDecision
proceedNovelty
53.70Framework unique
—Isolation
—Last stage change
2026-05-10 03:26:32Deduplication group #60605
Member of a group with 1 similar repo(s) — this repo is canonical view group →
Top concepts (1)
Project Description
Repobility · severity-and-effort ranking · https://repobility.com
AI Prompt
Create an R script to estimate canopy height using Open-Canopy principles. The project needs to handle two sources: IGN orthophotos (0.20m resolution, containing RVB + IRC bands) and pre-trained models from Open-Canopy (SPOT 1.5m). I need functions to download the necessary data, specifically loading IGN orthophotos and downloading Open-Canopy subsets. The core analysis should include calculating NDVI from the IRC band and running inference using the pre-trained models, ideally within a complete pipeline function that takes an Area of Interest (AOI) polygon as input. Please ensure the setup mentions prerequisites for `terra`, `sf`, and `reticulate` for Python integration.
r geospatial remote-sensing canopy-height image-processing pytorch huggingface ndvi
Generated by gemma4:latest
Catalog Information
This project, Open-Canopy R, estimates canopy height from IGN orthophotos and pre-trained models.
Description
Open-Canopy R is a project that utilizes pre-trained models to estimate canopy height from IGN orthophotos. The project adapts these models for use with higher-resolution IGN data (0.20m) compared to the original Open-Canopy dataset (1.5m). This allows for more accurate predictions and better understanding of forest canopies.
الوصف
هذا المشروع، Open-Canopy R، يقوم بتقدير ارتفاع الغابة من صور الأرض IGN و MODELS مدربة مسبقاً. يعدّل هذا المشروع هذه MODELS للاستخدام مع بيانات IGN ذات-resolution أعلى (0.20م) مقارنة بالبيانات الأصلية Open-Canopy (1.5م). مما يسمح بتقديرات أكثر دقة و فهم أفضل للغابات.
Novelty
7/10Tags
canopy-height-estimation ign-orthophotos pre-trained-models forest-canopies remote-sensing
Quality Score
F
46.1/100
Structure
35
Code Quality
35
Documentation
58
Testing
0
Practices
78
Security
100
Dependencies
60
Strengths
- Good security practices \u2014 no major issues detected
Weaknesses
- No LICENSE file \u2014 legal ambiguity for contributors
- No tests found \u2014 high risk of regressions
- No CI/CD configuration \u2014 manual testing and deployment
- 1 files with critical complexity need refactoring
- 1 'god files' with >500 LOC need decomposition
Recommendations
- Add a test suite \u2014 start with critical path integration tests
- Set up CI/CD (GitHub Actions recommended) to automate testing and deployment
- Add a linter configuration to enforce code style consistency
- Add a LICENSE file (MIT recommended for open source)
Security & Health
7.1h
Tech Debt (D)
A
OWASP (100%)
FAIL
Quality Gate
B
Risk (22)
Repobility · code-quality intelligence platform · https://repobility.com
Unknown
License
12.9%
Duplication
Languages
Frameworks
None detected
Concepts (1)
| Category | Name | Description | Confidence | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| auto_description | Project Description | Code R pour estimer la hauteur de canopée à partir des ortho IGN (RVB + IRC à 0.20m) en exploitant les modèles pré-entraînés Open-Canopy (SPOT 6-7 à 1.5m). | 80% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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