Reliable logistical planning must account for uncertainties, especially in humanitarian aid, where logistics form the backbone of almost all operations. In humanitarian missions, particularly in regions with few paved roads, access often depends on the condition of unpaved routes. Heavy rainfall can rapidly turn these into impassable tracks, and without reliable, up-to-date information on road conditions, planning becomes highly uncertain. This increases the risk of delays and failed operations where aid is needed most.
To address these challenges, the project pilots the AI Logistic Awareness System (AILAS), a weather-adaptive, AI-supported routing system for regions with unpaved roads (defined here as all routes without sealed surfaces such as asphalt, concrete, or paving stones). AILAS employs a convolutional neural network trained on street-level imagery to classify the passability of road segments at a given time. These classifications are combined with dynamic secondary data, such as rainfall and soil moisture, within a probabilistic model that predicts current road conditions using globally available geodata. The resulting assessments feed into routing software, enabling logistics planners to create dynamic routes that reflect both the current and anticipated passability of unpaved roads.
Street-level image data is initially collected using dashcams mounted on Croix-Rouge Malagasy (CRM) emergency vehicles along documented routes in Madagascar, the project’s pilot region. To broaden spatial coverage and reduce the need for extensive on-site data collection, additional imagery from the open-source platform Mapillary is integrated.structured way. All the data is then transferred to a database, and we meticulously assess its quality in terms of resolution, currentness, and more. At this point, we can precisely identify data gaps. These are addressed in the next step.
The captured street-level imagery (SLI) serves as the foundation for a convolutional neural network (CNN) that classifies road passability. Training data for the model is annotated through the crowdsourcing platform MapSwipe, which assigns different passability categories (e.g., “moderate” or “poor”). Quality assurance is ensured through expert validation carried out by the Croix-Rouge Malagasy.
The passability assessments derived from the CNN are coupled with globally available secondary data. Dynamic data includes frequently updated raster datasets such as precipitation and soil moisture, while static data comprises time-invariant environmental characteristics.
In this step, statistical analysis identifies the prognostic value of secondary data for road passability. On this basis, a probabilistic model predicts passability at specific points in time, using dynamic weather and static inputs. With sufficient SLI coverage, the model can extend predictions to the entire unpaved road network, including sections without direct image data.
In the final step, passability estimates from the prediction model are integrated into openrouteservice. Impassable or restricted roads are either excluded or penalized, and the adjusted unpaved network is connected to the broader transport system for practical routing. Integration is automated on a daily basis and made accessible through a web-based dashboard and an API.
The AILAS routing service wants to address potential users across various application fields:
Logistics departments in humanitarian organizations
Planners responsible for infrastructure and road development and maintenance
Municipal and governmental institutions
Commercial actors in industry and agriculture
With this pilot phase, we expect to test our methods in Madagascar. To scale the system to other regions, we aim to secure additional funding.
The system is expected to be applicable beyond the humanitarian sector. Anticipated use cases include assessing road passability under extreme conditions, which is of high relevance for economic logistics and infrastructure planning. The information generated could help prioritize the expansion of road segments particularly vulnerable to weather-related degradation. Further demand is expected in the transport of goods for extractive industries such as mining and forestry, which often rely on unpaved roads.
Looking ahead, the system could also be extended through simulation-based climate change scenarios. This would allow projected shifts in precipitation patterns or extreme events such as flooding, along with their possible impacts on the passability of specific road segments, to be meaningfully integrated into the system.
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Background imagery created with data from OpenStreetMap contributors