Spotting palm trees from space

The Earth Observation Applications department in Deimos Space UK develops systems and algorithms that derive information from satellite imagery. Watch Paula Marti explain how satellite data can be brought together and automatically processed to meet the needs of a customer using the example of SAFIY. There are many satellites providing data at different spatial and temporal resolution in different spectral bands (eg red, green, blue, near infra red). Satellite imagery analysis techniques can extract information from the data that cannot be necessarily seen by the naked eye, such as vegetation health or soil moisture content. In other scenarios, the information in the image can be easily interpreted by a human (eg finding buildings) but automation is needed in order to cover large areas (eg find all buildings in a city). Automated techniques tend to use powerful algorithms that are often derived from the robotics and computer vision sectors, and are also being used in new developments such as self-driving cars.One example of a Deimos project that creates information from satellite data is SAFIY. The aim of the project was to automate the production of geospatial information based on satellite data, to aid the planning and monitoring of urban change in support of UAE Government initiatives such as Smart Dubai. It also aimed to establish a strategy to facilitate data sharing primarily amongst government departments. Deimos Space UK worked together with Ordnance Survey International and in partnership with Mohammed Bin Rashid Space Centre (MBRSC).During the first phase of the project, SAFIY automated the extraction of features such as roads, water, vegetation and buildings from 1-metre resolution Earth Observation data acquired by the Dubaisat-2 and Deimos-2 satellites. During the second phase of the project, the focus was on monitoring vegetation, in particular mangrove forest and palm trees. In order to count individual palm trees higher resolution data at 30cm from WorldView-3 was used.Satellite imagery can cover large areas and also contain data in many spectral bands. This means these data files are big, ranging from a few gigabytes each to more than 100GB. Large amounts of computer storage are required, together with memory and processing power capable of running the algorithms and capacity to move or download large images for processing. The most effective way of working with this volume of data is to use cloud computing resources. This gives access to configurable resources which can be rapidly provisioned over the Internet.