arXiv (All)
open access
Earth Observation Satellite Scheduling Models: Taxonomy, Classification and Open Challenges
Over the past three decades, a large number of optimization models have been proposed for Earth observation satellite (EOS) scheduling. The main goal of this paper is to provide an overview of these models and to relate them to the characteristics of the problems they address. To do so, we first ide
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Geospatial solutions for a changing world : exploring satellite intelligence for economics#142429
- Infra-Bench CLS: A Global, Open-Source Benchmark for Critical Infrastructure Classification with Earth Observation Foundation Models#987224
- AlayaWorld: Interactive Long-Horizon World Modeling - Full Technical Report (v1.1)#451324
- Improving global precipitation forecasts with an AI weather model trained on satellite observations#1227445
- Toward Trainable AI Agents for Satellite Operations#814137
Record · ID 1387789
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive