scholarly journals A New Hybrid Water Balance and Machine Learning Approach for Groundwater Withdrawal Prediction using Integrated Multi-Temporal Remote Sensing Datasets

2020 ◽  
Author(s):  
Sayantan Majumdar ◽  
Ryan Glen Smith ◽  
James J Butler ◽  
Venkataraman Lakshmi
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Water Balance ◽  
Groundwater Withdrawal ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Multi Temporal

scholarly journals JUNGLE-NET: USING EXPLAINABLE MACHINE LEARNING TO GAIN NEW INSIGHTS INTO THE APPEARANCE OF WILDERNESS IN SATELLITE IMAGERY

2021 ◽  
Vol V-3-2021 ◽  
pp. 317-324
Author(s):  
T. Stomberg ◽  
I. Weber ◽  
M. Schmitt ◽  
R. Roscher
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Land Cover ◽  
Observational Data ◽  
Satellite Imagery ◽  
Learning Approach ◽  
Scene Classification ◽  
Machine Learning Approach ◽  
New Knowledge

Abstract. Explainable machine learning has recently gained attention due to its contribution to understanding how a model works and why certain decisions are made. A so far less targeted goal, especially in remote sensing, is the derivation of new knowledge and scientific insights from observational data. In our paper, we propose an explainable machine learning approach to address the challenge that certain land cover classes such as wilderness are not well-defined in satellite imagery and can only be used with vague labels for mapping. Our approach consists of a combined U-Net and ResNet-18 that can perform scene classification while providing at the same time interpretable information with which we can derive new insights about classes. We show that our methodology allows us to deepen our understanding of what makes nature wild by automatically identifying simple concepts such as wasteland that semantically describes wilderness. It further quantifies a class’s sensitivity with respect to a concept and uses it as an indicator for how well a concept describes the class.

A Machine Learning Approach to Cloud Masking in Sentinel-3 SLSTR Data

2020 ◽  
Author(s):  
Samuel Jackson ◽  
Jeyarajan Thiyagalingam ◽  
Caroline Cox
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Land Surface ◽  
Satellite Images ◽  
Binary Classification ◽  
Gradient Boosting ◽  
Learning Approach ◽  
Persistent Problem ◽  
Machine Learning Approach ◽  
Level 1

<p><span>Clouds appear ubiquitously in the Earth's atmosphere, and thus present a persistent problem for the accurate retrieval of remotely sensed information. The task of identifying which pixels are cloud, and which are not, is what we refer as the cloud masking problem. The task of cloud masking essentially boils down to assigning a binary label, representing either "cloud" or "clear", to each pixel. </span></p><p><span>Although this problem appears trivial, it is often complicated by a diverse number of issues that affect the imagery obtained from remote sensing instruments. For instance, snow, sea ice, dust, smoke, and sun glint can easily challenge the robustness and consistency of any cloud masking algorithm. The cloud masking problem is also further complicated by geographic and seasonal variation in acquired scenes. </span></p><p><span>In this work, we present a machine learning approach to handle the problem of cloud masking for the Sea and Land Surface Temperature Radiometer (SLSTR) on board the Sentinel-3 satellites. Our model uses Gradient Boosting Decision Trees (GBDTs), to perform pixel-wise segmentation of satellite images. The model is trained using a hand labelled dataset of ~12,000 individual pixels covering both the spatial and temporal domains of the SLSTR instrument and utilises the combined channels of the dual-view swaths. Pixel level annotations, while lacking spatial context, have the advantage of being cheaper to obtain compared to fully labelled images, a major problem in applying machine learning to remote sensing imagrey.</span></p><p><span>We validate the performance of our mask using cross validation and compare its performance with two baseline models provided in the SLSTR level 1 product. We show up to 10% improvement in binary classification accuracy compared with the baseline methods. Additionally, we show that our model has the ability to distinguish between different classes of cloud to reasonable accuracy.</span></p>

Sign in / Sign up

Export Citation Format

Share Document