Tutorials/Post - Remote Sensing, GIS, Earth System, Geo-AI/ML

Surface Water Detected by the BCE Algorithm

Deep Learning approach for Sentinel-1 Surface Water Mapping leveraging Google Earth Engine

Satellite remote sensing plays an important role in mapping the location and extent of surface water. A variety of approaches are available for mapping surface water, but deep learning approaches are not commonplace as they are ’data hungry’ and require large amounts of computational resources. However, with the availability of various satellite sensors and rapid development in cloud computing, the remote sensing scientific community is adapting modern deep learning approaches. The new integration of the cloud-based Google AI platform and Google Earth Engine enables users to deploy calculations at scale. In this paper, we investigate two methods of automatic data labeling: 1. the Joint Research Centre (JRC) surface water maps; 2. an Edge-Otsu dynamic threshold approach. We deployed a U-Net convolutional neural network to map surface water from Sentinel-1 Synthetic Aperture Radar (SAR) data and tested the model performance using different hyperparameter tuning combinations to identify the optimal learning rate and loss function. The performance was then evaluated using an independent validation data set. We tested 12 models overall and found that the models utilizing the JRC data labels showed a better model performance, with F1-scores ranging from 0.972-0.986 for the training test and validation efforts. Additionally, an independently sampled high-resolution data set was used to further evaluate model performance. From this independent validation effort, we observed models leveraging JRC data labels produced F1-Scores ranging from 0.9130.922. A pairwise comparison of models, through varying input data, learning rates, and loss functions constituents, revealed the JRC Adjusted Binary Cross Entropy Dice model to be statistically different than the 66 other model combinations and displayed the highest relative evaluations metrics including accuracy, precision score, Cohen Kappa coefficient, and F1-score. These results are in the same range as many of the conventional methods. We observed that the integration of Google AI Platform into Google Earth Engine can be a powerful tool to deploy deep-learning algorithms at scale and that automatic data labeling can be an effective strategy in the development of deep-learning models, however independent data validation remains an important step in model evaluation.

Read the full paper here:

Previous

Mapping sugarcane in Thailand using transfer learning, a lightweight convolutional neural network, NICFI high resolution satellite imagery and Google Earth Engine

2 Comments

  1. Khaled ALKHALED

    hi im doctor khaled ALKHALED i would like you share this paper with me

Leave a Reply

Powered by WordPress & Theme by Anders Norén

%d bloggers like this: