All precision agriculture services start with accurate field boundaries and seeded acres. Unfortunately, the problem is that one is making critical decisions based on inaccurate data, cadastral field boundary data, which is affecting the entire agricultural value chain. DigiFarm has spent the last two years developing a deep-resolution algorithm for Sentinel-2 imagery to increase image resolution by 10x from 10m to 1m, coupled with a deep neural network model to automatically detect field boundaries on a large scale. DigiFarm has delineated over 15 million ha across the world and has achieved an average (IoU) accuracy of above 0.96, which represents up to 20% greater accuracy than existing cadastral map data. DigiFarm delivers four key models to B2B and B2G clients through various API endpoints, including: automatic field boundary detection (and seeded acres), deep resolution of Sentinel-2 at 1m resolution on demand, automatic delineation of in-field productivity zones and crop classification, the ability to automatically detect field boundaries (seeded acres) using deep neural network models and super-high-resolution satellite imagery.

Benefits:

• Deep neural network model to delineate field boundaries and seeded acres
• Deep neural network model for the deep resolution of Sentinel-2 (10x) to 1m per pixel resolution, achieving sub-metre georeference accuracy
• Packaged products in APIs with simple integration and setup
• Easy SaaS pricing for B2B and B2G clients starting at EUR 0.03 per hectare/year

Only 20% of the world’s road network is paved, making it particularly vulnerable to environmental, weather and vehicle-related damage. The monitoring of unpaved roads is of high relevance as they are also used to transport people and goods. There are currently no IT-based methods that can be used to continuously record and analyse the condition of the road over a wide area.
Within the framework of the Tikal project, BAREWAYS GmbH is developing a software system that can determine the current and future road condition by means of pattern recognition, even from inhomogeneous and incomplete data sets. For this purpose, a wide variety of data sources such as weather forecasts, maps, satellite images, and vehicle sensors are aggregated, calibrated and analysed with the help of Artificial Intelligence (AI). This current road condition information is made available to the vehicle navigation system together with other criteria for route planning and optimisation such as vehicle type, fragility of the load or safety needs of the driver.

Benefits:

• Prediction of road conditions in rural areas that are not covered today
• The fusion of vehicle sensors, satellite imagery, and weather forecasts
• Flexible routing to account for individual vehicle capabilities
• Applicable to other transport lines such as railways and power lines

Modern transport and logistics require real-time tracking and monitoring of shipping containers worldwide. Many applications on the market track only the location of the vessel on which a container is transported – not the container itself. Therefore, the tracking signal is lost when the container is transferred between ships, trains, trucks and aircraft, or if it happens to be lost at sea. Callwise Ltd. addresses these issues using an innovative combination of satellite communication and a 5G LoRaWAN (Low Range Wide Area Network) IoT solution. This solution enables real-time tracking and monitoring of individual shipping containers from start to finish during multimodal transport. The concept is based on a novel architecture in which satellite and cellular tracking is combined with LoRaWAN tracking and monitoring of individual shipping containers. Callwise Ltd. is targeting logistics and shipping companies that can offer this end-to-end solution to customers as a value-added service in their product portfolios. Furthermore, the system is able to generate alerts for a wide range of events (unauthorised door opening, deviation from a predefined route, changes in container temperature, etc.) and also track down containers lost at sea.

Benefits:

• Real-time location information on shipping containers from start to finish
• Ability to monitor several parameters inside containers (e.g. temperature, air pressure, air moisture)
• Ability to trace containers that are lost at sea