We empower land-use planners and infrastructure managers to make informed investment decisions.
Geoneon’s solution team expands your internal data science capability to help you optimise geospatial data acquisition, asset monitoring and maintenance. Our advice relies on scalable geospatial data in combination with robust advanced analytics.
We believe that a proactive approach together with strategic investments in smart engineering, careful land-use planning, and resilient design can better protect infrastructure assets and human lives from natural disasters.
Geoneon’s solution team comes from a broad range of science and engineering backgrounds, including remote sensing, physics, astronomy, applied mathematics, earth sciences, machine learning, computer vision, medical imaging, risk analysis, natural disasters.
Automated Segmentation of Buildings and Roads
Mapping infrastructure requires intensive effort and time, but it is a necessary step to assess the exposure of infrastructure to natural disasters. Geoneon has developed an automated segmentation model to detect and map buildings and roads using Convolutional Neural Network. The combination of high-resolution imagery and automated deep learning analysis can generate near real-time accurate maps.
Natural Hazard Susceptibility Classification
In collaboration with global experts, Geoneon uses a qualitative approach to identify areas susceptible to natural hazards at a regional scale. We use numerical models to distinguish specific pre-existing conditions classified based on scientific criteria. The result can be visualised in a susceptibility map localising over your entire region of interest, including potential sources and propagation areas for different natural disaster processes.
Exposure and Vulnerability Calculation
Geoneon use advanced analytics combining the classification of natural hazard susceptibility with infrastructure metrics to identify exposure and vulnerability hot spots of your assets. This process is replicable for different hazards, different asset-types, and different metrics in any area. By knowing the level of vulnerability of exposed assets, you can make informed decision about mitigation implementation, asset monitoring, maintenance planning, and future infrastructure location.
Automated Vegetation Mapping
Vegetation mapping is important for cities as a vital component of healthy and sustainable communities. It is also important for infrastructure maintenance, so to minimize risk of tree fall and wildfire. However, the mapping of vegetation is time consuming and requires extensive field work by certified scientists. Geoneon is currently developing an automated model to map vegetation type, size, and distance to critical infrastructure.