agribound

v0.1.3 safe
1.0
Low Risk

Agribound: Unified agricultural field boundary delineation from satellite imagery using geospatial foundation models, pre-trained segmentation, and embeddings.

⚠ Tarball exceeded 25 MB — source code analysis was limited to package metadata only.

🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of malicious activity, with all checks indicating minimal risk.

  • No network calls
  • No shell execution
  • No code obfuscation
  • No credential harvesting
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution patterns detected, indicating no immediate signs of malicious activities.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent related to code obfuscation.
  • Credentials: No credential harvesting patterns detected, suggesting no immediate threat of secret or credential theft.
  • Metadata: No red flags detected in the provided metadata.

📦 Package Quality Overall: Low (3.8/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://montimaj.github.io/agribound
  • Detailed PyPI description (33990 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 100 commits in montimaj/agribound
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository montimaj/agribound appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Sayantan Majumdar, Justin L. Huntington, Peter ReVelle, Soheil Nozari, Ryan G. Smith, M. F. Hasan, Matt Bromley, Jayden Atkin, Jeremy Rapp, Eric R. Jensen, David Ketchum, Samapriya Roy" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with agribound 
Create a web-based application that helps farmers and agricultural researchers visualize and analyze their land using satellite imagery. The app should leverage the 'agribound' Python package to delineate field boundaries automatically from satellite images. Here’s how you can structure your project:

1. **Project Setup**: Start by setting up a basic Flask or Django web framework to host your application. Ensure you have all necessary dependencies installed, including 'agribound', 'geopandas', and 'folium'. 
2. **User Interface**: Design a simple yet intuitive user interface where users can upload satellite images of their farmland. Provide options to select different types of satellite data (e.g., Sentinel-2, Landsat). Include a map view using Folium for better visualization.
3. **Field Boundary Delineation**: Utilize 'agribound' to process uploaded images and generate field boundaries. Integrate the package’s capabilities to perform automatic segmentation and embedding on the images to identify and outline distinct fields within the image.
4. **Interactive Map Features**: Display the processed results on an interactive map. Users should be able to zoom in/out, pan around, and click on individual fields to see more details like area size, vegetation health indices if available, etc.
5. **Data Export Options**: Allow users to export the delineated field boundaries as GeoJSON files for further analysis or integration into other GIS software.
6. **Advanced Features (Optional)**: Consider adding advanced features such as historical comparison of field boundaries over time, integration with weather APIs to provide climate information relevant to each field, or predictive analytics based on historical data and current conditions.
7. **Testing and Documentation**: Ensure thorough testing of all functionalities and create comprehensive documentation to guide users through the setup and usage of the application.