Identification of Wastewater CH4 Emission Sources with Computer Vision and Sentinel-2 Observations

Poster at the ESA Phi-week 2020

Transcript

1. Identification of Wastewater CH4 Emission Sources with Computer Vision and Sentinel-2 Observations Carlos Alberto Gómez Gonzalez Gerard Aceves Soley Marc Guevara Vilardell Kim Serradell Maronda ESA Phi-week 2020
2. Background • CH4 is a greenhouse gas not as prevalent in the atmosphere as CO2 but far more potent (IPCC report, 2019) • According to the Emissions Database for Global Atmospheric Research (EDGAR), a global emission inventory, the wastewater disposal sector accounted for 5 to 20% of the CH4 emitted in 2012 (Janssens-Maenhout et al., 2019) • Current emission inventories lack spatial representativeness due to the usage of the population density to allocate emissions from wastewater treatment plants (WWTP) EDGAR emissions dataset
3. Aims of this study • Develop a Deep Learning-based methodology for the identification and localization of Spanish CH4 emission sources, with a focus on WWTPs • Using European Space Agency images from the Sentinel-2 mission • Design a methodology that could be applied to other geographical areas where the identification of emission sources is more scarce • Enhance the spatial representativeness of current emission inventories/databases
4. Aim of the project The Urban Waste Water Treatment database (UWWT-DB) is used for extracting the location of the WWTPs in Spain
5. Object detection Object Detection is one of the fundamental, and therefore well studied, computer vision problems
6. Object detection Li et al. 2019 Detecting objects on satellite images differs from the task of object detection on natural images. Examples:
7. TensorFlow object detection API • In this study we use Tensorflow, an open source machine learning platform • It facilitates the construction, training and deployment of object detection models • Contains state-of-art models that can be fine-tuned to learn new classes of objects Two Object Detection models considered (both on top of the ResNet50 backend): 1. Faster R-CNN 640x640 resnet50. Ren et al. (2015) 2. EfficientDet 640x640 resnet50. Tan et al. (2019)
8. Workflow Satellite images Training and validation sets (90%) Test set (10%) 3x Model training Parameters: batch size step size learning rate l.r. decay epochs WWTP detector (fine-tuned object detection model with a new class) Model evaluation Object detection models pre-trained (COCO 2017 dataset)
9. Sentinel-2 images • Red, Green, Blue bands at 10m resolution • 100 km x 100 km images • Earth imagery every 5 days • The characteristic size of the WWTP (meters to tens of meters) presents a challenge to the usage of Sentinel-2 images
10. Creating the dataset Sentinel-2 bands (R,G,B) Image of the WWTP WWTP coordinates from the UWWTP-DB Extract an window of 1x1 km, join 3 bands Bounding Boxes Label manually each image (using LabelImg) lon lat -4.913.763.548.201.410 4.647.908.205.880.020 -1.171.405.003.801.900 4.415.578.654.792.910 -3.191.401.053.333.200 4.408.318.555.398.290 -1.867.400.114.640.220 4.135.515.700.517.950 . . . Image file Annotation file
11. Sentinel 2 vs. Google Earth A Google Earth dataset was created containing the same WWTPs for a comparison with the Sentinel-2 counterpart. • Left: Sentinel-2 image (10 m resolution) • Right: Google Earth image (1 m resolution)
12. Predictions format The highest rated score prediction is chosen amongst the multiple predictions 0: [x1 y1 x2 y2 ] score 0 1: [x1 y1 x2 y2 ] score 1 2: [x1 y1 x2 y2 ] score 2 3: ...
13. Evaluation metrics Intersection Over Union (IOU) Usually, a valid prediction corresponds to a IOU > 0.5
14. Evaluation metrics Precision - Recall curve mean Average Precision (mAP) for different IOU thresholds
15. Results • 500 WWTP samples in total (90% for training/validation and 10% for testing) • mAP on the test set for IOU > 0.5, taking the mean over 3 runs with different train/test shuffles With the Sentinel-2 images lower detection performance is obtained. Still the results are decent considering the WWTP test the resolution limits of the instrument. mAP (Google Earth data) mAP (Sentinel-2 data) Faster R-CNN 0,79 0,62 EfficientDet 0,89 0,70
16. Prediction examples on Sentinel-2 images It would be extremely hard to find the wastewater treatment plants by eye
17. Prediction examples on Google Earth images
18. Conclusions and next steps • State-of-the-art object detection models give promising results in localizing wastewater plants even at the 10m resolution of Sentinel-2 images • Expand the training dataset with more examples (from the UWWT-DB) • A simple analysis shows a relationship between the plant size and its water treatment capacity (Pearson correlation coefficient: 0,64) • Couple a regression model to predict the plant capacity • Use WWTP capacity as proxy for CH4 emissions to improve the existing methodologies for spatial distribution of emissions
19. Thank you carlos.gomez@bsc.es This study has been possible thanks to the BSC International Summer HPC Internship Programme and the work of Gerard Aceves Soley during his internship.