Real-time Geodata Analysis Powers Logistical Challenges in Military Land Operations

Russia’s war of aggression in Ukraine has clearly demonstrated the importance of logistical capabilities. Especially at the beginning, the Russian side had considerable problems getting supplies to the front due to a variety of factors. There were repeated images of convoys with supplies that were stuck or even abandoned.

The Ukrainian armed forces have further intensified their efforts to combat Russian supply vehicles and convoys. Both ammunition depots and supply vehicles are critical targets, as they ensure that combat troops are supplied with vital resources. NATO Secretary General Jens Stoltenberg has described the war in Ukraine as a “battle for ammunition.”

The Challenge of Modern Military Logistics

In addition to manufacturing large quantities of consumables, efficient distribution to the fighting troops poses a completely different critical challenge, particularly in view of today’s modular troop structure and the high mobility of combat troops. If the logistical supply of weapon systems is compromised, the system becomes ineffective in combat and loses its operational value.

Software solutions with real-time situational and geodata analysis can be used to overcome these challenges and make the supply of units more efficient and robust. These solutions can already support manned and autonomous vehicles, paving the way for fully automated logistics.

Enhancing Logistics with Geospatial Analysis

The following example illustrates how purely software-based real-time geodata analysis can contribute to improving system supply.

In addition to assessing the enemy situation, evaluating geofactors is also an essential part of operational planning. The quality of a planned route depends not only on the possible speed, but also on the viable alternatives in case of unexpected events such as roadblocks.

Calculating Robust Routes

With the help of a newly developed geoanalysis from Carmenta, terrain data can be used to calculate spaces and routes that are more robust, thereby improving mission reliability. In general, robustness is a measure of the loss of freedom of movement when an unwanted event or hazard occurs that prevents the vehicle from continuing its planned mission.

In the image, the robust areas are shown in green and the non-robust areas in orange/red. It is clear to see that the road by the lake is not robust. If, for example, a barrier is activated here, it is highly likely that the mission cannot be continued or, in the worst case, the vehicle must be left behind.

The non-robust areas can now be dynamically and automatically taken into account or avoided during route planning. This makes battlefield supply more robust and significantly increases the probability of successful supply.

However, the robustness of the battlefield is not only of interest for route planning. Potentially favorable areas for barriers can also be identified in order to restrict the enemy’s freedom of movement as much as possible.

Dynamic Supply Point Calculation for Mobile Forces

Modern weapon systems are characterized by high mobility and speed, which makes planning supply points that can be reached safely and on time increasingly complex. Waiting times pose a considerable risk for platforms and weapon systems. Ideally, the supply vehicle should arrive at the supply point at the same time as the weapon system in order to avoid waiting times as far as possible.

In addition, there may be hazards and impassable areas along the supply vehicle’s route that delay its arrival time and make it impossible to reach the previously planned supply point. These complex and dynamic requirements pose challenges for the manual planning of supply points.

Adapt to evolving operational demands

Real-time geo-analysis in conjunction with live position data can make a significant contribution to the dynamic calculation of supply points between supply vehicles and the troops to be supplied. Based on Carmenta’s Terrain router, a real-time calculation of supply points that can be reached safely and on time has been developed.

The system performs these calculations dynamically, incorporating tactical information and changes in the environment. As a result, new, time-accurate supply points are continuously and precisely generated to meet evolving operational demands. Such adaptability ensures a flexible and efficient supply chain for combat troops, even in rapidly changing battlefield conditions.

The figure shows the route of the weapon system in yellow. Possible supply points have been defined along this route. The time required for the weapon system to reach the defined supply points is also shown in yellow and in minutes.

Carmenta Engine calculates the optimal supply point and the most timely and safest route for the supply vehicle. The time required for the supply vehicle to reach the supply point is shown in black. The time spent by the supply vehicle in open terrain is shown in red. In the example shown, the supply vehicle reaches the supply point at the same time as the weapon system and spends only 43 seconds in open terrain. 

This calculation enables a fast and dynamic response to changes on the battlefield. By calculating supply routes and points in real time, the efficiency and safety of supply can be significantly increased. This is particularly important in unpredictable and rapidly changing situations where flexibility and quick decision-making are essential.

The role of up-to-date, high-resolution geodata

The accuracy of geo-analysis depends heavily on the timeliness and resolution of the available geodata. Satellite images often offer only limited timeliness, viewing angles, and resolution. Today, AI tools and drones can help generate up-to-date, high-resolution geodata. Carmenta and SE3 Labs are working together to create terrain classifications from current image data using AI. 

Real-time UAV data is transformed into high-resolution, semantic 3D maps using AI-assisted computer vision. SE3 Labs integrates 3D reconstruction, object recognition, and scene-based analysis via the local language model “SpatialGPT.” This unique technology dynamizes geodata analysis by creating detailed terrain classifications and continuously updating geodata. The advantage: robust, near-live navigability maps and terrain analyses that are directly integrated into the Carmenta Engine.

This enables real-time mission planning and adaptation to detected changes based on geodata updated every minute. Together with the SpatialGPT voice interface, this creates an intuitive, precise, and adaptive basis for mission planning—particularly relevant for autonomous and UGV-supported operations.

Read more about AI-Powered Geospatial Intelligence