Towards large-scale mapping of local climate zones using multitemporal Sentinel 2 data and convolutional neural networks
| Autor: | Johannes Rosentreter, Ron Hagensieker, Björn Waske |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
010504 meteorology & atmospheric sciences
Basis (linear algebra) Computer science business.industry 0208 environmental biotechnology Soil Science Geology Context (language use) Percentage point 02 engineering and technology Machine learning computer.software_genre 01 natural sciences Convolutional neural network 020801 environmental engineering Random forest Workflow Categorization Artificial intelligence Computers in Earth Sciences Scale (map) business computer 0105 earth and related environmental sciences Remote sensing |
| Zdroj: | Remote Sensing of Environment. 237:111472 |
| ISSN: | 0034-4257 |
| Popis: | In recent years, the concept of Local Climate Zones (LCZs) has become a new standard in the research of urban landscapes. LCZs outline a classification scheme, which is designed to categorize urban and rural surfaces according to their climate-relevant properties, irrespective of local building materials or cultural background. We present a novel workflow for a high-resolution derivation of LCZs using multi-temporal Sentinel 2 (S2) composites and supervised Convolutional Neural Networks (CNNs). We assume that CNNs, due to their potential invariance to size and illumination of objects, are best suited to predict the highly context-based LCZs on a large scale. As a first step, the proposed workflow includes a fully automated generation of cloud-free S2 composites. These composites serve as training data basis for the LCZ classifications carried out over eight German cities. Results show that by using a CNN, overall accuracies can be increased by an average of 16.5 and 4.8 percentage points when compared to a pixel-based and a texture-based Random Forest approach, respectively. If sufficient training data is available, CNN models proved to be robust in classifying unknown cities and achieved overall accuracies of up to 86.5 % . The proposed method constitutes a feasible approach for automated, large scale mapping of LCZs, and could be the preferred alternative for LCZ classifications in upcoming studies. |
| Databáze: | OpenAIRE |
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