The extreme latitude of the Arctic generates some specific challenges that are exclusive to this area of the world. Although they are all technically solvable, a comprehensive solution must take them all into consideration. Some of the challenges are:
• The very large seasonal difference in light conditions that affects optical data acquisition which is basically possible only for half of the year
• The presence of false alarms in the vessel detection due to the presence of sea-ice and icebergs in motion, and
• The default geographic information in datum and projection that ignores deformations in extreme latitudes.
The iceberg detection, and its discrimination from ships by SAR, is first of all crucial for navigation safety and, at the second time, for the identification of “dark vessels” that can be spotted with combination of AIS (e.g. Air Transmission gap detection) and Sentinel 1.
In order to overcome the necessity to analyse huge areas, AIS data can be used to detect anomalous behaviour of vessels and provide useful insights both on the vessels by detecting: Anchoring, a vessel that is stationary for a long period in open sea; Rendezvous, two vessels meeting in open sea for a long period
Also in this case, the Arctic region provides challenges like the presence of the icepack during winter months that can bring the vessel to follow a very narrow route thus resulting in false alarms in rendezvous detection. For this reason, the analysis allow to filter the events depending on the attributes specific of the event (e.g. type of vessels involved, speed and direction).
In the case of land change, the difficult conditions of Arctic (illumination, seasonality, cloud presence) gives huge challenges for the use of optical data. SAR sensors have been thus be experimented to detect possible anomalies and in general for all monitoring purposes. In general, the progress will be based on:
• Detection of changes using both HR images (Sentinel 1) and VHR images (Cosmo-Skymed, Terrasar, Iceye) and in particular MTC, ACD and CCD maps in a continuous way
• Detection of routes in the ice-pack and of populated areas (to be further investigated)
• Automatic change detection in a specific Area of Interest (to be further investigated) using EO data and available open datasets like OpenStreetMap
• Detection of Atmospheric pollution anomalies to provide alerting
• An analysis of Social Media and News sources through language analysis and filtering (to be further investigated).
