Built on

M. C. Hansen, P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland
2013
Earlier work this paper cites.
E. Cai, D.-C. Juan, D. Stamoulis, and D. Marculescu, “Neuralpower: Predict and deploy energy-efficient convolutional neural networks,” in
2017
Earlier work this paper cites.
D. Lam, R. Kuzma, K. McGee, S. Dooley, M. Laielli, M. Klaric, Y. Bulatov, and B. McCord, “xview: Objects in context in overhead imagery,”
Original
2018
Earlier work this paper cites.
G. Christie, N. Fendley, J. Wilson, and R. Mukherjee, “Functional map of the world,” in
2018
Earlier work this paper cites.
R. Gupta, B. Goodman, N. Patel, R. Hosfelt, S. Sajeev, E. Heim, J. Doshi, K. Lucas, H. Choset, and M. Gaston, “Creating xbd: A dataset for assessing building damage from satellite imagery,” in
2019
Earlier work this paper cites.
G. Sumbul, M. Charfuelan, B. Demir, and V. Markl, “Bigearthnet: A large-scale benchmark archive for remote sensing image understanding,” in
2019
Earlier work this paper cites.
F. Paolo, T.-t. T. Lin, R. Gupta, B. Goodman, N. Patel, D. Kuster, D. Kroodsma, and J. Dunnmon, “xview3-sar: Detecting dark fishing activity using synthetic aperture radar imagery,”
2022
Earlier work this paper cites.
X. Sun, P. Wang, Z. Yan, F. Xu, R. Wang, W. Diao, J. Chen, J. Li, Y. Feng, T. Xu
2022
Earlier work this paper cites.
I. Tsoumas, G. Giannarakis, V. Sitokonstantinou, A. Koukos, D. Loka, N. Bartsotas, C. Kontoes, and I. Athanasiadis, “Evaluating digital tools for sustainable agriculture using causal inference,”
Original
2022
Earlier work this paper cites.
S. Hong, X. Zheng, J. Chen, Y. Cheng, J. Wang, C. Zhang, Z. Wang, S. K. S. Yau, Z. Lin, L. Zhou
Original
2023
Earlier work this paper cites.
Q. Wu, G. Bansal, J. Zhang, Y. Wu, S. Zhang, E. Zhu, B. Li, L. Jiang, X. Zhang, and C. Wang, “Autogen: Enabling next-gen llm applications via multi-agent conversation framework,”
Original
2023
Earlier work this paper cites.
Y. Hu, J. Yuan, C. Wen, X. Lu, and X. Li, “Rsgpt: A remote sensing vision language model and benchmark,”
Original
2023
Earlier work this paper cites.
Y. Jian, F. Yu, S. Singh, and D. Stamoulis, “Stable diffusion for aerial object detection,”
Original
2023
Earlier work this paper cites.
N. I. Bountos, A. Ouaknine, and D. Rolnick, “Fomo-bench: a multi-modal, multi-scale and multi-task forest monitoring benchmark for remote sensing foundation models,”
Original
2023
Earlier work this paper cites.
V. G. Goecks and N. R. Waytowich, “Disasterresponsegpt: Large language models for accelerated plan of action development in disaster response scenarios,”
Original
2023
Earlier work this paper cites.
E. Commission and J. R. Centre,
2023
Earlier work this paper cites.
M. Pesaresi and P. Politis,
2023
Earlier work this paper cites.
Y. Zhuang, Y. Yu, K. Wang, H. Sun, and C. Zhang, “Toolqa: A dataset for llm question answering with external tools,”
2023
Earlier work this paper cites.
V. K. Srinivasan, Z. Dong, B. Zhu, B. Yu, D. Mosk-Aoyama, K. Keutzer, J. Jiao, and J. Zhang, “Nexusraven: a commercially-permissive language model for function calling,” in
2023
Earlier work this paper cites.
R. Roscher, M. Rußwurm, C. Gevaert, M. Kampffmeyer, J. A. dos Santos, M. Vakalopoulou, R. Hänsch, S. Hansen, K. Nogueira, J. Prexl
Original
2023
Earlier work this paper cites.
D. Kroodsma, J. Turner, C. Luck, T. Hochberg, N. Miller, P. Augustyn, and S. Prince, “Global prevalence of setting longlines at dawn highlights bycatch risk for threatened albatross,”
2023
Earlier work this paper cites.

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K. Kuckreja, M. S. Danish, M. Naseer, A. Das, S. Khan, and F. S. Khan, “Geochat: Grounded large vision-language model for remote sensing,” in
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Y. Zhan, Z. Xiong, and Y. Yuan, “Skyeyegpt: Unifying remote sensing vision-language tasks via instruction tuning with large language model,”
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Then

G. Yang, Y. Li, Y. He, Z. Zhou, L. Ye, H. Fang, Y. Luo, and X. Feng, “Multimodal large language model for wheat breeding: a new exploration of smart breeding,”
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2024
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A. Lacoste, N. Lehmann, P. Rodriguez, E. Sherwin, H. Kerner, B. Lütjens, J. Irvin, D. Dao, H. Alemohammad, A. Drouin
2024
Later among the works it cites.
X. X. Zhu, Z. Xiong, Y. Wang, A. J. Stewart, K. Heidler, Y. Wang, Z. Yuan, T. Dujardin, Q. Xu, and Y. Shi, “On the foundations of earth and climate foundation models,”
Original
2024
Later among the works it cites.
Z. Xiong, Y. Wang, F. Zhang, A. J. Stewart, J. Hanna, D. Borth, I. Papoutsis, B. Le Saux, G. Camps-Valls, and X. X. Zhu, “Neural plasticity-inspired foundation model for observing the earth crossing modalities,”
2024
Later among the works it cites.

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Reading the bibliography…

2025

Multi-Agent Geospatial Copilots for Remote Sensing Workflows

Lee, Chaehong, Paramanayakam, Varatheepan, Karatzas, Andreas et al.

Understand

We present GeoLLM-Squad, a geospatial Copilot that introduces the novel multi-agent paradigm to remote sensing (RS) workflows.

Unlike existing single-agent approaches that rely on monolithic large language models (LLM), GeoLLM-Squad separates agentic orchestration from geospatial task-solving, by delegating RS tasks to specialized sub-agents.
Built on the open-source AutoGen and GeoLLM-Engine frameworks, our work enables the modular integration of diverse applications, spanning urban monitoring, forestry protection, climate analysis, and agriculture studies.
Our results demonstrate that while single-agent systems struggle to scale with increasing RS task complexity, GeoLLM-Squad maintains robust performance, achieving a 17% improvement in agentic correctness over state-of-the-art baselines.

Built on

M. C. Hansen, P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland
2013
Earlier work this paper cites.
E. Cai, D.-C. Juan, D. Stamoulis, and D. Marculescu, “Neuralpower: Predict and deploy energy-efficient convolutional neural networks,” in
2017
Earlier work this paper cites.
D. Lam, R. Kuzma, K. McGee, S. Dooley, M. Laielli, M. Klaric, Y. Bulatov, and B. McCord, “xview: Objects in context in overhead imagery,”
Original
2018
Earlier work this paper cites.
G. Christie, N. Fendley, J. Wilson, and R. Mukherjee, “Functional map of the world,” in
2018
Earlier work this paper cites.
R. Gupta, B. Goodman, N. Patel, R. Hosfelt, S. Sajeev, E. Heim, J. Doshi, K. Lucas, H. Choset, and M. Gaston, “Creating xbd: A dataset for assessing building damage from satellite imagery,” in
2019
Earlier work this paper cites.
G. Sumbul, M. Charfuelan, B. Demir, and V. Markl, “Bigearthnet: A large-scale benchmark archive for remote sensing image understanding,” in
2019
Earlier work this paper cites.
F. Paolo, T.-t. T. Lin, R. Gupta, B. Goodman, N. Patel, D. Kuster, D. Kroodsma, and J. Dunnmon, “xview3-sar: Detecting dark fishing activity using synthetic aperture radar imagery,”
2022
Earlier work this paper cites.
X. Sun, P. Wang, Z. Yan, F. Xu, R. Wang, W. Diao, J. Chen, J. Li, Y. Feng, T. Xu
2022
Earlier work this paper cites.
I. Tsoumas, G. Giannarakis, V. Sitokonstantinou, A. Koukos, D. Loka, N. Bartsotas, C. Kontoes, and I. Athanasiadis, “Evaluating digital tools for sustainable agriculture using causal inference,”
Original
2022
Earlier work this paper cites.
S. Hong, X. Zheng, J. Chen, Y. Cheng, J. Wang, C. Zhang, Z. Wang, S. K. S. Yau, Z. Lin, L. Zhou
Original
2023
Earlier work this paper cites.
Q. Wu, G. Bansal, J. Zhang, Y. Wu, S. Zhang, E. Zhu, B. Li, L. Jiang, X. Zhang, and C. Wang, “Autogen: Enabling next-gen llm applications via multi-agent conversation framework,”
Original
2023
Earlier work this paper cites.
Y. Hu, J. Yuan, C. Wen, X. Lu, and X. Li, “Rsgpt: A remote sensing vision language model and benchmark,”
Original
2023
Earlier work this paper cites.
Y. Jian, F. Yu, S. Singh, and D. Stamoulis, “Stable diffusion for aerial object detection,”
Original
2023
Earlier work this paper cites.
N. I. Bountos, A. Ouaknine, and D. Rolnick, “Fomo-bench: a multi-modal, multi-scale and multi-task forest monitoring benchmark for remote sensing foundation models,”
Original
2023
Earlier work this paper cites.
V. G. Goecks and N. R. Waytowich, “Disasterresponsegpt: Large language models for accelerated plan of action development in disaster response scenarios,”
Original
2023
Earlier work this paper cites.
E. Commission and J. R. Centre,
2023
Earlier work this paper cites.
M. Pesaresi and P. Politis,
2023
Earlier work this paper cites.
Y. Zhuang, Y. Yu, K. Wang, H. Sun, and C. Zhang, “Toolqa: A dataset for llm question answering with external tools,”
2023
Earlier work this paper cites.
V. K. Srinivasan, Z. Dong, B. Zhu, B. Yu, D. Mosk-Aoyama, K. Keutzer, J. Jiao, and J. Zhang, “Nexusraven: a commercially-permissive language model for function calling,” in
2023
Earlier work this paper cites.
R. Roscher, M. Rußwurm, C. Gevaert, M. Kampffmeyer, J. A. dos Santos, M. Vakalopoulou, R. Hänsch, S. Hansen, K. Nogueira, J. Prexl
Original
2023
Earlier work this paper cites.
D. Kroodsma, J. Turner, C. Luck, T. Hochberg, N. Miller, P. Augustyn, and S. Prince, “Global prevalence of setting longlines at dawn highlights bycatch risk for threatened albatross,”
2023
Earlier work this paper cites.

Similar

K. Kuckreja, M. S. Danish, M. Naseer, A. Das, S. Khan, and F. S. Khan, “Geochat: Grounded large vision-language model for remote sensing,” in
2024
Cited alongside, same era.
Y. Zhan, Z. Xiong, and Y. Yuan, “Skyeyegpt: Unifying remote sensing vision-language tasks via instruction tuning with large language model,”
Original
2024
Cited alongside, same era.
S. Singh, M. Fore, and D. Stamoulis, “Evaluating tool-augmented agents in remote sensing platforms,”
Original
2024
Cited alongside, same era.
H. Guo, X. Su, C. Wu, B. Du, L. Zhang, and D. Li, “Remote sensing chatgpt: Solving remote sensing tasks with chatgpt and visual models,” in
2024
Cited alongside, same era.
S. Singh, M. Fore, and D. Stamoulis, “Geollm-engine: A realistic environment for building geospatial copilots,” in
2024

Then

G. Yang, Y. Li, Y. He, Z. Zhou, L. Ye, H. Fang, Y. Luo, and X. Feng, “Multimodal large language model for wheat breeding: a new exploration of smart breeding,”
Original
2024
Later among the works it cites.
A. Lacoste, N. Lehmann, P. Rodriguez, E. Sherwin, H. Kerner, B. Lütjens, J. Irvin, D. Dao, H. Alemohammad, A. Drouin
2024
Later among the works it cites.
X. X. Zhu, Z. Xiong, Y. Wang, A. J. Stewart, K. Heidler, Y. Wang, Z. Yuan, T. Dujardin, Q. Xu, and Y. Shi, “On the foundations of earth and climate foundation models,”
Original
2024
Later among the works it cites.
Z. Xiong, Y. Wang, F. Zhang, A. J. Stewart, J. Hanna, D. Borth, I. Papoutsis, B. Le Saux, G. Camps-Valls, and X. X. Zhu, “Neural plasticity-inspired foundation model for observing the earth crossing modalities,”
2024
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Cited alongside, same era.

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2024

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2024

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Z. Z. Wang, J. Mao, D. Fried, and G. Neubig, “Agent workflow memory,”

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2024