Tool
Amazon.ia — Inteligencia Ambiental
A developing Amazon-wide biodiversity monitoring initiative that aims to combine field observations, satellite data and AI to inform conservation and restoration decisions.
Tool
Another way to view our planet: OlmoEarth embeddings
Author or publisher unknown · Publication date unknown
What it covers
Ai2's interactive guide demonstrates OlmoEarth satellite embeddings through landscape similarity, change detection and land-cover examples, and links to a free, no-sign-in global embeddings explorer.
Who it helps
Intended audience has not been documented.
Limitations
Interactive guide reviewed; linked Explorer not functionally tested. Examples and performance claims not independently validated. Free access refers to the Explorer, not every OlmoEarth service.
Show citation
Author unknown. Date unknown. Another way to view our planet: OlmoEarth embeddings. https://narrative.allen.ai/olmoearth-embeddings (AI & Environment Resource Hub; record tool-olmoearth-embeddings-guide; collection snapshot 2026-10-24).
Classification and context
Included in the original Tool collection. Labels below are metadata based suggestions. They are not verified findings, claims of effectiveness, or endorsements.
lifecycle
orientation
Unclassified
Available source metadata
- Author or creator
- Unknown or not supplied
- Publisher
- Unknown or not supplied
- Publication date
- Unknown or not supplied
- Date precision
- unknown
- Geographic scope
- Unknown
- Access
- free
- Language
- unknown
- Peer review
- unknown
- Source access
- Not checked
- Metadata confidence
- Metadata unconfirmed
- Last verified
- Not yet verified
- Snapshot import
- 2026-10-24
- Legacy domain
- Biodiversity & Ecosystems
- Legacy subdomain
- Unknown or not supplied
- source evidence
- https://narrative.allen.ai/olmoearth-embeddings
- metadata reviewed at
- 2026-10-23
- review evidence
- https://narrative.allen.ai/olmoearth-embeddings
- context reviewed at
- 2026-10-23
Provenance and review
- Source access
- Not checked
- Metadata confidence
- Metadata unconfirmed
- Editorial relevance
- AI for environmental action
- Evidence quality
- Evidence quality has not been separately assessed.
Source sheet: Tool · Row 344 · Original ID: Not supplied.
Inclusion does not establish effectiveness, maintenance, or endorsement. Check access and current documentation. Source link has not yet been checked. The import date is not the original date added.
Suggest a correction for this record →Newsletter appearances
Publication, placement, and evidence are tracked separately. A legacy coverage flag alone does not prove an appearance.
- Issue 64 · draft · resource list
User explicitly requested issue 64 logging on October 23, 2026; publication not confirmed.
Related through shared environmental topics
Ordered by the number of shared provisional topic labels. This indicates a browsing connection, not agreement between sources.
Shared topics: Research tools and geospatial methods; Biodiversity and ecosystems
Shared topics: Research tools and geospatial methods; Biodiversity and ecosystems
Organization · North America
Coolant Climate
Founded at Harvard and MIT, Coolant is building forestry MRV that is as accurate as LiDAR at the price of hand measurement. We use only aerial drone footage to model reforestation sites with cutting edge computer vision techniques and calculate individual tree carbon stock. In recent studies with the Ocean Foundation, we've reached over 95% accuracy of LiDAR for their mangrove restoration sites.
Shared topics: Research tools and geospatial methods; Biodiversity and ecosystems
Tool
CoralSense
UC Berkeley capstone project using machine learning and an interactive map to model coral reef health under environmental scenarios.
Shared topics: Research tools and geospatial methods; Biodiversity and ecosystems
Scientific Paper · 2026-10-16
Domain-specific language alignment of multimodal embeddings towards explainable predictions
This preprint develops a method for connecting multimodal Earth embeddings to domain-specific language, helping explain species-distribution predictions through habitat descriptions and interpretable ecological concepts.