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A. F. Giraldo-Forero, J. A. Jaramillo-Garzón, and C. G. Castellanos-Domínguez, “Evaluation of example-based measures for multi-label classification performance,” in Bioinformatics and Biomedical Engineering: Third International Conference, IWBBIO 2015, Granada, Spain, April 15-17, 2015, Proceedings, Part I 3 . Springer, 2015, pp. 557–564
2015
Earlier work this paper cites.
N. Gorelick, M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, and R. Moore, “Google earth engine: Planetary-scale geospatial analysis for everyone,” Remote sensing of Environment , vol. 202, pp. 18–27, 2017
2017
Earlier work this paper cites.
K. Guu, K. Lee, Z. Tung, P. Pasupat, and M. Chang, “Retrieval augmented language model pre-training,” in International conference on machine learning . PMLR, 2020, pp. 3929–3938
2020
Earlier work this paper cites.
Z. Wang, S. Zhou, D. Fried, and G. Neubig, “Execution-based evaluation for open-domain code generation,” arXiv preprint arXiv:2212.10481 , 2022
Original
2022
Earlier work this paper cites.
S. Yao, J. Zhao, D. Yu, N. Du, I. Shafran, K. Narasimhan, and Y. Cao, “React: Synergizing reasoning and acting in language models,” arXiv preprint arXiv:2210.03629 , 2022
Original
2022
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,” arXiv preprint arXiv:2307.15266 , 2023
Original
2023
Earlier work this paper cites.
J. Bai, S. Bai, Y. Chu, Z. Cui, K. Dang, X. Deng, Y. Fan, W. Ge, Y. Han, F. Huang et al. , “Qwen technical report,” arXiv preprint arXiv:2309.16609 , 2023
Original
2023
Earlier work this paper cites.
Y. Lai, C. Li, Y. Wang, T. Zhang, R. Zhong, L. Zettlemoyer, W.-t. Yih, D. Fried, S. Wang, and T. Yu, “Ds-1000: A natural and reliable benchmark for data science code generation,” in International Conference on Machine Learning . PMLR, 2023, pp. 18 319–18 345
2023
Earlier work this paper cites.
N. Muennighoff, Q. Liu, A. Zebaze, Q. Zheng, B. Hui, T. Y. Zhuo, S. Singh, X. Tang, L. Von Werra, and S. Longpre, “Octopack: Instruction tuning code large language models,” arXiv preprint arXiv:2308.07124 , 2023
Original
2023
Earlier work this paper cites.
X. Chen, M. Lin, N. Schärli, and D. Zhou, “Teaching large language models to self-debug,” arXiv preprint arXiv:2304.05128 , 2023
Original
2023
Earlier work this paper cites.
J. Li, G. Li, C. Tao, H. Zhang, F. Liu, and Z. Jin, “Large language model-aware in-context learning for code generation,” arXiv preprint arXiv:2310.09748 , 2023
Original
2023
Earlier work this paper cites.
A. Patel, S. Reddy, D. Bahdanau, and P. Dasigi, “Evaluating in-context learning of libraries for code generation,” arXiv preprint arXiv:2311.09635 , 2023
Original
2023
Earlier work this paper cites.
F. Zhang, B. Chen, Y. Zhang, J. Keung, J. Liu, D. Zan, Y. Mao, J.-G. Lou, and W. Chen, “Repocoder: Repository-level code completion through iterative retrieval and generation,” arXiv preprint arXiv:2303.12570 , 2023
Original
2023
Earlier work this paper cites.
C. E. Jimenez, J. Yang, A. Wettig, S. Yao, K. Pei, O. Press, and K. Narasimhan, “Swe-bench: Can language models resolve real-world github issues?” arXiv preprint arXiv:2310.06770 , 2023
Original
2023
Earlier work this paper cites.
K. Zhang, H. Zhang, G. Li, J. Li, Z. Li, and Z. Jin, “Toolcoder: Teach code generation models to use api search tools,” arXiv preprint arXiv:2305.04032 , 2023
Original
2023
Earlier work this paper cites.
Z. Li and H. Ning, “Autonomous gis: the next-generation ai-powered gis,” International Journal of Digital Earth , vol. 16, no. 2, pp. 4668–4686, 2023
2023
Earlier work this paper cites.
R. Manvi, S. Khanna, G. Mai, M. Burke, D. Lobell, and S. Ermon, “Geollm: Extracting geospatial knowledge from large language models,” arXiv preprint arXiv:2310.06213 , 2023
Original
2023
Earlier work this paper cites.
Z. Lin, C. Deng, L. Zhou, T. Zhang, Y. Xu, Y. Xu, Z. He, Y. Shi, B. Dai, Y. Song et al. , “Geogalactica: A scientific large language model in geoscience,” arXiv preprint arXiv:2401.00434 , 2023
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,” arXiv preprint arXiv:2307.15266 , 2023
Original
2023
Earlier work this paper cites.

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P. P. Ray, “Chatgpt: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope,” Internet of Things and Cyber-Physical Systems , vol. 3, pp. 121–154, 2023
2023
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S. Zhang, Z. Chen, Y. Shen, M. Ding, J. B. Tenenbaum, and C. Gan, “Planning with large language models for code generation,” arXiv preprint arXiv:2303.05510 , 2023
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2024

An LLM Agent for Automatic Geospatial Data Analysis

Chen, Yuxing, Wang, Weijie, Lobry, Sylvain et al.

Understand

Large language models (LLMs) are being used in data science code generation tasks, but they often struggle with complex sequential tasks, leading to logical errors.

Their application to geospatial data processing is particularly challenging due to difficulties in incorporating complex data structures and spatial constraints, effectively utilizing diverse function calls, and the tendency to hallucinate less-used geospatial libraries.
To tackle these problems, we introduce GeoAgent, a new interactive framework designed to help LLMs handle geospatial data processing more effectively.
GeoAgent pioneers the integration of a code interpreter, static analysis, and Retrieval-Augmented Generation (RAG) techniques within a Monte Carlo Tree Search (MCTS) algorithm, offering a novel approach to geospatial data processing.

Built on

A. F. Giraldo-Forero, J. A. Jaramillo-Garzón, and C. G. Castellanos-Domínguez, “Evaluation of example-based measures for multi-label classification performance,” in Bioinformatics and Biomedical Engineering: Third International Conference, IWBBIO 2015, Granada, Spain, April 15-17, 2015, Proceedings, Part I 3 . Springer, 2015, pp. 557–564
2015
Earlier work this paper cites.
N. Gorelick, M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, and R. Moore, “Google earth engine: Planetary-scale geospatial analysis for everyone,” Remote sensing of Environment , vol. 202, pp. 18–27, 2017
2017
Earlier work this paper cites.
K. Guu, K. Lee, Z. Tung, P. Pasupat, and M. Chang, “Retrieval augmented language model pre-training,” in International conference on machine learning . PMLR, 2020, pp. 3929–3938
2020
Earlier work this paper cites.
Z. Wang, S. Zhou, D. Fried, and G. Neubig, “Execution-based evaluation for open-domain code generation,” arXiv preprint arXiv:2212.10481 , 2022
Original
2022
Earlier work this paper cites.
S. Yao, J. Zhao, D. Yu, N. Du, I. Shafran, K. Narasimhan, and Y. Cao, “React: Synergizing reasoning and acting in language models,” arXiv preprint arXiv:2210.03629 , 2022
Original
2022
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,” arXiv preprint arXiv:2307.15266 , 2023
Original
2023
Earlier work this paper cites.
J. Bai, S. Bai, Y. Chu, Z. Cui, K. Dang, X. Deng, Y. Fan, W. Ge, Y. Han, F. Huang et al. , “Qwen technical report,” arXiv preprint arXiv:2309.16609 , 2023
Original
2023
Earlier work this paper cites.
Y. Lai, C. Li, Y. Wang, T. Zhang, R. Zhong, L. Zettlemoyer, W.-t. Yih, D. Fried, S. Wang, and T. Yu, “Ds-1000: A natural and reliable benchmark for data science code generation,” in International Conference on Machine Learning . PMLR, 2023, pp. 18 319–18 345
2023
Earlier work this paper cites.
N. Muennighoff, Q. Liu, A. Zebaze, Q. Zheng, B. Hui, T. Y. Zhuo, S. Singh, X. Tang, L. Von Werra, and S. Longpre, “Octopack: Instruction tuning code large language models,” arXiv preprint arXiv:2308.07124 , 2023
Original
2023
Earlier work this paper cites.
X. Chen, M. Lin, N. Schärli, and D. Zhou, “Teaching large language models to self-debug,” arXiv preprint arXiv:2304.05128 , 2023
Original
2023
Earlier work this paper cites.
J. Li, G. Li, C. Tao, H. Zhang, F. Liu, and Z. Jin, “Large language model-aware in-context learning for code generation,” arXiv preprint arXiv:2310.09748 , 2023
Original
2023
Earlier work this paper cites.
A. Patel, S. Reddy, D. Bahdanau, and P. Dasigi, “Evaluating in-context learning of libraries for code generation,” arXiv preprint arXiv:2311.09635 , 2023
Original
2023
Earlier work this paper cites.
F. Zhang, B. Chen, Y. Zhang, J. Keung, J. Liu, D. Zan, Y. Mao, J.-G. Lou, and W. Chen, “Repocoder: Repository-level code completion through iterative retrieval and generation,” arXiv preprint arXiv:2303.12570 , 2023
Original
2023
Earlier work this paper cites.
C. E. Jimenez, J. Yang, A. Wettig, S. Yao, K. Pei, O. Press, and K. Narasimhan, “Swe-bench: Can language models resolve real-world github issues?” arXiv preprint arXiv:2310.06770 , 2023
Original
2023
Earlier work this paper cites.
K. Zhang, H. Zhang, G. Li, J. Li, Z. Li, and Z. Jin, “Toolcoder: Teach code generation models to use api search tools,” arXiv preprint arXiv:2305.04032 , 2023
Original
2023
Earlier work this paper cites.
Z. Li and H. Ning, “Autonomous gis: the next-generation ai-powered gis,” International Journal of Digital Earth , vol. 16, no. 2, pp. 4668–4686, 2023
2023
Earlier work this paper cites.
R. Manvi, S. Khanna, G. Mai, M. Burke, D. Lobell, and S. Ermon, “Geollm: Extracting geospatial knowledge from large language models,” arXiv preprint arXiv:2310.06213 , 2023
Original
2023
Earlier work this paper cites.
Z. Lin, C. Deng, L. Zhou, T. Zhang, Y. Xu, Y. Xu, Z. He, Y. Shi, B. Dai, Y. Song et al. , “Geogalactica: A scientific large language model in geoscience,” arXiv preprint arXiv:2401.00434 , 2023
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,” arXiv preprint arXiv:2307.15266 , 2023
Original
2023
Earlier work this paper cites.

Similar

P. P. Ray, “Chatgpt: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope,” Internet of Things and Cyber-Physical Systems , vol. 3, pp. 121–154, 2023
2023
Cited alongside, same era.
S. Zhang, Z. Chen, Y. Shen, M. Ding, J. B. Tenenbaum, and C. Gan, “Planning with large language models for code generation,” arXiv preprint arXiv:2303.05510 , 2023
Original
2023
Cited alongside, same era.
S. Singh, M. Fore, and D. Stamoulis, “Geollm-engine: A realistic environment for building geospatial copilots,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops , June 2024, pp. 585–594
2024
Cited alongside, same era.
C. Liu, K. Chen, H. Zhang, Z. Qi, Z. Zou, and Z. Shi, “Change-agent: Toward interactive comprehensive remote sensing change interpretation and analysis,” IEEE Transactions on Geoscience and Remote Sensing , vol. 62, pp. 1–16, 2024
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