Vertical evidence

Albumentations evidence in Environment and conservation

Wildlife, biodiversity, conservation, habitat, forest, and pollution monitoring.

This page keeps broad public-source counts distinct from the named records below. They describe public evidence, not companies, customers, production deployments, or commercial licenses.

Elephant among trees in a forest — Illustrative imagePhoto: Sargaraj Tr on Pexels

Classified public GitHub repositories48Repository metadata matched to this vertical; classifications are multi-label and non-additive.View evidence Research works citing Albumentations17Public citations assigned to this vertical. Citation evidence is not deployment evidence.View evidence Competition records10Released-package competition records tagged to this vertical.View evidence

Verified Hugging Face artifacts3Public models, datasets, and Spaces with code-use or runtime-dependency evidence.View evidence

ML competitions

Winning solutions in Environment and conservation

Each competition keeps placement-level public sources and a source image linked back to the competition provider.

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Kaggle competition image for Understanding Clouds from Satellite Images

Image: Kaggle competition page

Kaggle · 2019

Understanding Clouds from Satellite Images

Can you classify cloud structures from satellites?

Environment and conservation Semantic segmentation

Placements: 6
Teams: 1,531
Prize pool: $10,000

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Kaggle competition image for Google Research - Identify Contrails to Reduce Global Warming

Image: Kaggle competition page

Kaggle · 2023

Google Research - Identify Contrails to Reduce Global Warming

Train ML models to identify contrails in satellite images and help prevent their formation

Environment and conservation Semantic segmentation

Placements: 5
Teams: 954
Prize pool: $50,000

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Kaggle competition image for CSIRO - Image2Biomass Prediction

Image: Kaggle competition page

Kaggle · 2026

CSIRO - Image2Biomass Prediction

Predict biomass using the provided pasture images

Agriculture and food Visual regression

Placements: 5
Teams: 3,805
Prize pool: $75,000

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Named public records

Inspect the evidence itself

These records are curated examples from the detailed snapshot. Their count can be lower than the broader source count above because the sources are collected and refreshed independently.

Exact public-code imports

Each named record identifies the context of an exact import. An import can appear in source, an example, a test, documentation, or a notebook.

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Import in source

IBM/ImpactMesh

Multimodal, multitemporal dataset for flood and wildfire prediction

Import in source

microsoft/MegaDetector-Overhead

MegaDetector-Overhead — The Microsoft open-source AI for overhead wildlife detection. Point-based detection model for aerial and drone imagery, identifying wildlife from above. Maintained by Microsoft AI for Good Lab. Part of the Pytorch-Wildlife ecosystem.