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This paper introduces a novel framework, Tree-GPT, which incorporates Large Language Models (LLMs) into the forestry remote sensing data workflow, thereby enhancing the efficiency of data analysis.
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Beloiu, M., Heinzmann, L., Rehush, N., Gessler, A., Griess, V. C., 2023. Individual Tree-Crown Detection and Species Identification in Heterogeneous Forests Using Aerial RGB Imagery and Deep Learning. Remote Sensing
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2019
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Sun, Y., Xin, Q., Huang, J., Huang, B., Zhang, H., 2019. Characterizing tree species of a tropical wetland in southern china at the individual tree level based on convolutional neural network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Weinstein, B. G., Marconi, S., Bohlman, S., Zare, A., White, E., 2019. Individual tree-crown detection in RGB imagery using semi-supervised deep learning neural networks. Remote Sensing
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Yang, Q., Su, Y., Jin, S., Kelly, M., Hu, T., Ma, Q., Li, Y., Song, S., Zhang, J., Xu, G. et al., 2019. The influence of vegetation characteristics on individual tree segmentation methods with airborne LiDAR data. Remote Sensing
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Lobry, S., Marcos, D., Murray, J., Tuia, D., 2020. RSVQA: Visual question answering for remote sensing data. IEEE Transactions on Geoscience and Remote Sensing
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Yang, J., Kang, Z., Cheng, S., Yang, Z., Akwensi, P. H., 2020. An individual tree segmentation method based on watershed algorithm and three-dimensional spatial distribution analysis from airborne LiDAR point clouds. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Chappuis, C., Zermatten, V., Lobry, S., Le Saux, B., Tuia, D., 2022. Prompt-rsvqa: Prompting visual context to a language model for remote sensing visual question answering. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 1372–1381
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Li, Y., Chai, G., Wang, Y., Lei, L., Zhang, X., 2022. Ace r-cnn: An attention complementary and edge detection-based instance segmentation algorithm for individual tree species identification using uav rgb images and lidar data. Remote Sensing
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2023
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Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., Fung, P., 2023. Survey of hallucination in natural language generation. ACM Computing Surveys
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