LuoJiaAI: A cloud-based artificial intelligence platform for remote sensing image interpretation

Figures & data

Figure 1. Developmental line of existing popular RS-CCPs.

Figure 2. A simplified platform diagram of LuoJiaAI.

Figure 3. Logical model of sample type.

Figure 4. Sample classification framework.

Figure 5. Flowchart of multi-source sample data integration.

Figure 6. Technical flow diagram of automatic/semi-automatic combined labeling.

Figure 7. Overall framework of the sample sharing service.

Figure 8. Dataset statistics in LuoJiaSET platform.

Figure 9. Sample query interface in LuoJiaSET platform.

Figure 10. Dataset information displayed in LuoJiaSET platform.

Figure 11. High-level service APIs in LuoJiaSET platform.

Figure 12. Simplified system architecture diagram for LuoJiaNET.

Figure 13. Process of IBGM method in memory scalability module for large-scale image processing.

Figure 14. Visualization of different large-scale image processing methods on GID.

Table 1. Land-use classification results on GID with HRNetV2 model.

Methods	MIoU (%)
	Background	Build-up	Farmland	Forest	Meadow	Water	Overall
HRNetV2	61.03	62.86	63.36	28.02	53.17	78.65	57.85
+ IBGM	63.46	63.93	64.56	29.13	53.25	80.29	59.10

Figure 15. Workflow of distributed parallel computing strategy.

Figure 16. Visualization of different large-scale image processing methods on GID.

Table 2. Land-use classification results on GID with FCN8s model.

Methods (Backbone=FCN8s)	Overall
	MIoU (%)
Patch-based Processing	57.85
Distributed Parallel Strategy	62.90

Figure 17. Process of adaptive optimization method for optimizing multi-scale and multi-channel image features.

Figure 18. Visualization of the adaptive optimization method on WHU-Hi.

Table 3. Land-use classification results on WHU-Hi with FreeNet model.

Methods	Overall
	OA (%)	Kappa (%)
FreeNet	94.47	94.02
+ Adaptive Optimization	96.01	95.90

Figure 19. Auto-parallel computing strategy embedding prior geographic knowledge.

Table 4. Scene classification results on WHU-RS19 with different parallel computing strategies.

Methods	Top1-acc (%)	Top5-acc (%)
ResNet-50	86.5	98.5
+ Auto-parallel (+ GLCM Input)	89.5	98.0
+ Auto-parallel (+ GLCM in Cost Function)	87.0	99.0

Figure 20. Overall architecture of the LuoJiaNET’s front-end application.

Figure 21. Main page of the front-end application, with object detection taken as an example.

Figure 22. Model database of LuoJiaNET.

Figure 23. Overall architecture of the LuoJiaNET’s visual modeling tool.

Figure 24. Main view of visual modeling tool.

Figure 25. Visualization on AID using ResNet-50 in scene classification task.

Figure 26. Visualization on DOTAV2.0 (Ding et al. 2021) using Mask-RCNN (He et al. 2017) in object detection task.

Figure 27. Visualization on ISPRS-Vaihingen (Rottensteiner et al. 2012) using U-Net (Ronneberger, Fischer, and Brox 2015) and DeepLabV3+ (Chen et al. 2018) in land-use classification task.

Figure 28. Visualization on WHU-BCD using DTCDSCN (Liu et al. 2020) in change detection task.

Figure 29. Visualization on WHU-Stereo (Li et al. 2022) using GC-Net (Ni et al. 2020) in multi-view 3D reconstruction task.

Ding, J., N. Xue, G. S. Xia, X. Bai, W. Yang, M. Yang, S. Belongie, J. Luo, M. Datcu, and M. Pelillo. 2021. “Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges.” IEEE Transactions on Pattern Analysis and Machine Intelligence 44: 7778–7796. doi:10.1109/TPAMI.2021.3117983.

 Web of Science ®Google Scholar

He, K., G. Gkioxari, P. Dollár, and R. Girshick. 2017. “Mask R-CNN.” Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, 2961–2969.

 Google Scholar

Rottensteiner, F., G. Sohn, J. Jung, M. Gerke, C. Baillard, S. Benitez, and U. Breitkopf. 2012. “The ISPRS Benchmark on Urban Object Classification and 3D Building Reconstruction.” ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences I-3 (2012), Nr. 1 1 (1): 293–298. doi:10.5194/isprsannals-I-3-293-2012.

 Google Scholar

Ronneberger, O., P. Fischer, and T. Brox. 2015. “U-Net: Convolutional Networks for Biomedical Image Segmentation.” International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, Cham. Munich, Germany, 234–241.

 Google Scholar

Chen, L. C., Y. Zhu, G. Papandreou, F. Schroff, and H. Adam. 2018. “Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation.” Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, 801–818.

 Google Scholar

Liu, Y., C. Pang, Z. Zhan, X. Zhang, and X. Yang. 2020. “Building Change Detection for Remote Sensing Images Using a Dual-Task Constrained Deep Siamese Convolutional Network Model.” IEEE Geoscience and Remote Sensing Letters 18 (5): 811–815. doi:10.1109/LGRS.2020.2988032.

 Web of Science ®Google Scholar

Li, S., S. He, S. Jiang, W. Jiang, and L. Zhang. 2022. “WHU-Stereo: A Challenging Benchmark for Stereo Matching of High-Resolution Satellite Images.” arXiv preprint arXiv:2206.02342.

 Google Scholar

Ni, J., J. Wu, J. Tong, Z. Chen, and J. Zhao. 2020. “GC-Net: Global Context Network for Medical Image Segmentation.” Computer Methods and Programs in Biomedicine 190: 105121. doi:10.1016/j.cmpb.2019.105121.

 PubMed Web of Science ®Google Scholar

Data availability statement

The data that support the findings of this study are available on GitHub at https://github.com/WHULuoJiaTeam.