People of Interest
Konstantin Klemmer
[Geospatial Analysis] Specializing in geospatial AI for urban planning and environmental modeling.
Scientific Paper · source-verified
Deep Learning and Satellite Remote Sensing for Biodiversity Monitoring and Conservation
Nathalie Pettorelli; Jake Williams; Henrike Schulte to Bühne; Merry Crowson · 2024-06-17
Deep learning combined with satellite remote sensing enhances biodiversity monitoring and conservation by leveraging big data, but challenges in capacity building, data access, environmental costs, and model interpretability must be addressed for broader adoption.
Show citation
Nathalie Pettorelli; Jake Williams; Henrike Schulte to Bühne; Merry Crowson. 2024-06-17. Deep Learning and Satellite Remote Sensing for Biodiversity Monitoring and Conservation. https://zslpublications.onlinelibrary.wiley.com/doi/10.1002/rse2.415 (AI & Environment Resource Hub; record paper-080; collection snapshot 2026-09-15).
Classification and context
Included in the original Scientific Paper collection. Labels below are metadata based suggestions. They are not verified findings, claims of effectiveness, or endorsements.
topic
lifecycle
orientation
Available source metadata
- Author or creator
- Nathalie Pettorelli; Jake Williams; Henrike Schulte to Bühne; Merry Crowson
- Publisher
- Deep Learning, Biodiversity Monitoring, Conservation
- Publication date
- 2024-06-17
- Date precision
- day
- Geographic scope
- Unknown
- Language
- unknown
- Peer review
- unknown
- Source status
- verified
- Last verified
- 2026-09-15T16:58:06Z
- Snapshot import
- 2026-09-06
- Legacy domain
- Biodiversity & Ecosystems
- Legacy subdomain
- Deep Learning and Satellite Remote Sensing for Biodiversity Conservation
- journal
- Deep Learning, Biodiversity Monitoring, Conservation
Provenance and review
Source sheet: Scientific Paper · Row 84 · Original ID: paper-080.
A paper listing is not a quality assessment. Peer review and findings require source-level confirmation. Source link reachable · checked 2026-09-15. Source identity and required metadata verified. The import date is not the original date added.
Suggest a correction for this record →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: Cross-cutting sustainability; Research tools and geospatial methods; Biodiversity and ecosystems; Built environment and infrastructure
Shared topics: Cross-cutting sustainability; Research tools and geospatial methods; Biodiversity and ecosystems; Built environment and infrastructure
People of Interest
Nikola Milojević-Dupont
[Geospatial Analysis] Applying AI to building stock data for urban sustainability and energy efficiency.
Shared topics: Cross-cutting sustainability; Research tools and geospatial methods; Built environment and infrastructure
Podcast · 2026-08-05
20 Gigawatts in Orbit: Starcloud's Plan for AI Compute
Philip Johnston is the co-founder and CEO of Starcloud, the company building data centers in space. In November 2025, Starcloud launched an Nvidia H100 GPU into orbit and trained the first large language model in space. They've since raised $200 million, hit a billion-dollar valuation just 17 months after YC demo day and filed with the FCC to deploy 88,000 more satellites. In this episode, Philip walks us through their wild origin story, the engineering challenges behind the Starcloud-1, why they booked a SpaceX launch before they even knew what they were building and how data centers in space make sense both economically and politically.
Shared topics: Cross-cutting sustainability; Research tools and geospatial methods; Biodiversity and ecosystems
Scientific Paper · 2025-07-18
A Call for Transdisciplinary Trust in the AI Era
Trust is a cornerstone and enabler of human civilization, determining the very nature of how people interact with each other. The swift integration of artificial intelligence (AI) into daily life poses grand societal challenges and necessitates a reevaluation of trust. Our bibliometric literature review calls for scientists and stakeholders to cross traditional academic boundaries to address emerging and evolving societal challenges arising from AI. We propose a transdisciplinary research framework to understand and bolster trust in AI and address grand challenges in domains as diverse and urgent as misinformation, discrimination, and warfare.