Noisy Supervision for Correcting Misaligned Cadaster Maps Without Perfect Ground Truth Data

International audienceIn machine learning the best performance on a certain task is achieved by fully supervised methods when perfect ground truth labels are available. However, labels are often noisy, especially in remote sensing where manually curated public datasets are rare. We study the multi-modal cadaster map alignment problem for which available annotations are mis-aligned polygons, resulting in noisy supervision. We subsequently set up a multiple-rounds training scheme which corrects the ground truth annotations at each round to better train the model at the next round. We show that it is possible to reduce the noise of the dataset by iteratively training a better alignment model to correct the annotation alignment

Map-Repair: Deep Cadastre Maps Alignment and Temporal Inconsistencies Fix in Satellite Images

In the fast developing countries it is hard to trace new buildings construction or old structures destruction and, as a result, to keep the up-to-date cadastre maps. Moreover, due to the complexity of urban regions or inconsistency of data used for cadastre maps extraction, the errors in form of misalignment is a common problem. In this work, we propose an end-to-end deep learning approach which is able to solve inconsistencies between the input intensity image and the available building footprints by correcting label noises and, at the same time, misalignments if needed. The obtained results demonstrate the robustness of the proposed method to even severely misaligned examples that makes it potentially suitable for real applications, like OpenStreetMap correction

Map-Repair: Deep Cadastre Maps Alignment and Temporal Inconsistencies Fix in Satellite Images

In the fast developing countries it is hard to trace new buildings construction or old structures destruction and, as a result, to keep the up-to-date cadastre maps. Moreover, due to the complexity of urban regions or inconsistency of data used for cadastre maps extraction, the errors in form of misalignment is a common problem. In this work, we propose an end-to-end deep learning approach which is able to solve inconsistencies between the input intensity image and the available building footprints by correcting label noises and, at the same time, misalignments if needed. The obtained results demonstrate the robustness of the proposed method to even severely misaligned examples that makes it potentially suitable for real applications, like OpenStreetMap correction

Polygonal Building Segmentation by Frame Field Learning

While state of the art image segmentation models typically output segmentations in raster format, applications in geographic information systems often require vector polygons. To help bridge the gap between deep network output and the format used in downstream tasks, we add a frame field output to a deep segmentation model for extracting buildings from remote sensing images. We train a deep neural network that aligns a predicted frame field to ground truth contours. This additional objective improves segmentation quality by leveraging multi-task learning and provides structural information that later facilitates polygonization; we also introduce a polygonization algorithm that utilizes the frame field along with the raster segmentation. Our code is available at https://github.com/Lydorn/Polygonization-by-Frame-Field-Learning.Comment: CVPR 2021 - IEEE Conference on Computer Vision and Pattern Recognition, Jun 2021, Pittsburg / Virtual, United State

Noisy Supervision for Correcting Misaligned Cadaster Maps Without Perfect Ground Truth Data

International audienceIn machine learning the best performance on a certain task is achieved by fully supervised methods when perfect ground truth labels are available. However, labels are often noisy, especially in remote sensing where manually curated public datasets are rare. We study the multi-modal cadaster map alignment problem for which available annotations are mis-aligned polygons, resulting in noisy supervision. We subsequently set up a multiple-rounds training scheme which corrects the ground truth annotations at each round to better train the model at the next round. We show that it is possible to reduce the noise of the dataset by iteratively training a better alignment model to correct the annotation alignment

Land Disturbance Extraction in Alberta Oil Sands Satellite Imagery

Tracking and mapping land disturbances from oil and gas development are critical to environmental assessments and land reclamation. Manual extraction of land disturbances is costly, time-consuming, and requires updating given rapid development. Through this study, a methodology and model for oil and gas land disturbance extraction along with an error-correcting algorithm solving rural mapping deficiencies is proposed. Chapter 3 titled “Deep-learning Extraction of Land Disturbances Arising from Oil and Gas Development” explores and finds an optimal strategy for land disturbance extraction methodology. Outlining the 3 findings in Chapter 3 for land disturbance extraction: (1) road and wellpad extraction should be integrated into a single task, (2) land disturbance extraction task should be segmented between forest and farmland backgrounds, and (3) RGB outperforms NDVI in land disturbance extraction.Chapter 4 titled “Maintaining Accurate Maps of Rural Land Disturbances: A Deep-Learning Automatic Change Detection Algorithm” introduces and tests the proposed error-correcting algorithm and explores its hyperparameters. Results in Chapter 4 show that the proposed automated error-correcting algorithm improves performance by 8.3% − 15.4% compared to baseline. Key findings in Chapter 4 explore how hyperparameters affect model performance: (1) alpha and beta in the AEC algorithm need to be carefully selected, (2) careful selection of alpha and beta can reduce the number of transitory artifacts introduced, (3) running the AEC algorithm a few times can greatly improve model performance, (4) adding a threshold to when the AEC algorithm begins stabilizes model performance. Combining findings in Chapters 3 and 4, an accurate fully automatic alternative to manual mapping for oil and gas land disturbance extraction is proposed. The combined model proposed in Chapter 4 is a ready solution to tracking, mapping, and managing land disturbances in the Alberta oil sands for purposes of environmental assessments and land reclamation