On the Importance of Feature Representation for Flood Mapping using Classical Machine Learning Approaches

by   Kevin Iselborn, et al.

Climate change has increased the severity and frequency of weather disasters all around the world. Flood inundation mapping based on earth observation data can help in this context, by providing cheap and accurate maps depicting the area affected by a flood event to emergency-relief units in near-real-time. Building upon the recent development of the Sen1Floods11 dataset, which provides a limited amount of hand-labeled high-quality training data, this paper evaluates the potential of five traditional machine learning approaches such as gradient boosted decision trees, support vector machines or quadratic discriminant analysis. By performing a grid-search-based hyperparameter optimization on 23 feature spaces we can show that all considered classifiers are capable of outperforming the current state-of-the-art neural network-based approaches in terms of total IoU on their best-performing feature spaces. With total and mean IoU values of 0.8751 and 0.7031 compared to 0.70 and 0.5873 as the previous best-reported results, we show that a simple gradient boosting classifier can significantly improve over deep neural network based approaches, despite using less training data. Furthermore, an analysis of the regional distribution of the Sen1Floods11 dataset reveals a problem of spatial imbalance. We show that traditional machine learning models can learn this bias and argue that modified metric evaluations are required to counter artifacts due to spatial imbalance. Lastly, a qualitative analysis shows that this pixel-wise classifier provides highly-precise surface water classifications indicating that a good choice of a feature space and pixel-wise classification can generate high-quality flood maps using optical and SAR data. We make our code publicly available at: https://github.com/DFKI-Earth-And-Space-Applications/Flood_Mapping_Feature_Space_Importance


page 6

page 15

page 16


M^3VSNet: Unsupervised Multi-metric Multi-view Stereo Network

The present MVS methods with deep learning have an impressive performanc...

EddyNet: A Deep Neural Network For Pixel-Wise Classification of Oceanic Eddies

This work presents EddyNet, a deep learning based architecture for autom...

NPF-MVSNet: Normal and Pyramid Feature Aided Unsupervised MVS Network

We proposed an unsupervised learning-based network, named NPF-MVSNet, fo...

Same Features, Different Day: Weakly Supervised Feature Learning for Seasonal Invariance

"Like night and day" is a commonly used expression to imply that two thi...

Water Preservation in Soan River Basin using Deep Learning Techniques

Water supplies are crucial for the development of living beings. However...

Geo-Tiles for Semantic Segmentation of Earth Observation Imagery

To cope with the high requirements during the computation of semantic se...

Resource saving taxonomy classification with k-mer distributions and machine learning

Modern high throughput sequencing technologies like metagenomic sequenci...

Code Repositories



view repo

Please sign up or login with your details

Forgot password? Click here to reset