Sufian Zaabalawi, Autor auf Blog

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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Harvesting Ship’s Speed and Environmental Information

May 25, 2021 by Sufian Zaabalawi Leave a Comment

Filed Under: 52°North, Deep Learning, Geostatistics, Machine Learning Tagged With: ais, data analytics, data mining, data scraping, machine learning, ML, speed over ground, vessel traffic, weather routing

The MariData project was recently launched to improve the energy efficiency of ship operations and to reduce emissions. Fuel consumption regarding vessel traffic is affected by many factors, namely, the main and auxiliary engines, the propulsion system, ship hull, propellers, seakeeping performance, as well as weather and sea conditions. In order to tackle these problems, we divide the task of building a weather routing system into subproblems and handle each subproblem separately. Finding the correlations between the speed of the vessel and weather conditions would lead to a better understanding of the weather conditions affecting fuel consumption, therefore, we obtained speed data of the ship’s Automatic Identification Systems (AIS), then retrieved weather and sea information for each data point using timestamp and geographic locations. The combination of these data sources enabled us to build a model to better understand the inherent relationships.

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