Communities Archive - Page 11 of 80 - Blog

Short introduction to the OGC API – Processes – Part 1: Core

April 20, 2022 by Benjamin Pross Leave a Comment

Filed Under: 52°North, Communities, Geoprocessing Tagged With: OAPIProc, OGC API Processes, WPS2

In January, the Open Geospatial Consortium (OGC) released Part 1 of the OGC API – Processes standard. As one of the editors, I would like to provide a short introduction to the API.

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Hydrological time series forecasting with Neural Networks

April 20, 2022 by Sebastian Drost 2 Comments

Filed Under: 52°North, Deep Learning, Geostatistics Tagged With: Deep Learning, hydrology, Neural Networks, Time series forecasting

Bochum students explore deep learning techniques for rainfall runoff predictions

In recent years, the applicability of Machine Learning approaches for hydrological use cases has reached great attendance. Especially long short-term memory (LSTM) networks show comparable results to conceptual models when being applied to hydrological time series forecasting problems. Now, Interdisciplinary students from Bochum explored innovative deep learning techniques for rainfall-runoff modeling.

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Flood detection using Sentinel 1 images

November 5, 2021 by Sufian Zaabalawi Leave a Comment

Filed Under: 52°North, Earth Observation Tagged With: EO, Flood detection, ML, water recognition, Water Surface Classification

In a previous Blog-post, we implemented an application to identify water surfaces using Synthetic Aperture Radar (SAR) images produced by radar-based sensors. The main advantage of using this type of sensor is that it can overcome the limitation of optical spectral sensors in the case of clouds and weather conditions that block spectral reflectance. An example is the Sentinel 1 satellite, which uses microwaves that can penetrate haze, light rain and clouds to illuminate the earth and measure the backscatter and travel time of the transmitted waves reflected by objects on the ground. It has the clear advantage of being far less dependent on the weather and can acquire images at night to obtain near-real-time information and monitor the earth over a large area. Furthermore, SAR images provide a lower backscatter reflectance for water surfaces as compared to non-water surfaces. This significant difference can be used to distinguish water surfaces and other land areas.

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Water Surface Classification in Landsat 8 and Sentinel 1 Images

September 28, 2021 by Sufian Zaabalawi Leave a Comment

Filed Under: 52°North, Earth Observation Tagged With: Deep Learning algorithm, Landsat, Sentinel, Water Surface Classification

Deep Learning algorithms are being used more often to process remote sensing data. Data captured from satellite platforms requires reliable and generalized methods to periodically extract and analyse useful information. Environmental planners are becoming increasingly interested in applications, such as land cover mapping ranging from urban development to crop detection, to monitor climate change and wildfires.

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