{"id":3275,"date":"2024-10-14T08:29:12","date_gmt":"2024-10-14T08:29:12","guid":{"rendered":"https:\/\/www.go-inicio.com\/?p=3275"},"modified":"2025-03-25T13:33:18","modified_gmt":"2025-03-25T13:33:18","slug":"a-new-look-revolutionizing-solar-plant-prospection-with-automated-viewshed-analysis-2","status":"publish","type":"post","link":"https:\/\/www.go-inicio.com\/en\/a-new-look-revolutionizing-solar-plant-prospection-with-automated-viewshed-analysis-2","title":{"rendered":"A new look: Revolutionizing Solar Plant Prospection with Automated Viewshed Analysis"},"content":{"rendered":"\n

Context<\/h2>\n\n

Inicio\u2019s promise is to identify the best locations for solar power plants using Eywa, our in-house algorithm. While there is an increasing need to develop Europe renewable energy production, finding places where this activity is compatible with the local contraints is not easy. <\/p>\n\n

This is especially true for Inicio where we aim at finding medium to large scale projects that will respect and bring synergies to the local agricultural activity. We hence need to follow numerous and strict conditions to avoid disrupting the surrounding area. They can be related to town planning, agricultural planing, protected areas, etc\u2026 One of them is visibility from close housing and historical monuments. <\/p>\n\n

Since this is a hard constraint, as a solar power plant developer you will aim to identify this risk as soon as possible when looking for the perfect place. Ideally in the early phases of your research and before having to go on the field. But how can you do that without seeing the surrounding area by yourself? <\/p>\n\n

Visibility contraints are painful<\/h3>\n\n

Most of the solar project developers are using tools like Google Earth, QGIS, ArcGIS, etc\u2026 to predict if there will be visibility issues.<\/p>\n\n

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Figure 1: We want to know which areas in the selected zone are visible from housing in the area<\/figcaption><\/figure>\n\n

This process is quite slow and not really precise as you will need to guess which areas of your future project will be visible, or manually process huge datasets by yourself. In the best case scenario you will ask a GIS (Geographic Information System) expert to perform a \u201cViewshed analysis\u201d (more explanations in the next section) but this process will still be manual and highly dependent on your datasets quality. <\/p>\n\n

At Inicio, we need to quickly predict if an area will be suitable for solar projects, using our in-house tech solutions. This enables our prospection team to travel through France to meet the lands private owners and local authorities. Since we found a large number of leads across the territory, we don\u2019t want to waste their time travelling to a location where there are important visibility issues. <\/p>\n\n

That\u2019s why we decided to develop a fully-automated pipeline to identify which areas are visible from every building and monument on France territory, and provide the prospection team with a viewshed analysis tool giving precise visibility analysis in less than a second.<\/p>\n\n

Running a viewshed analysis requires technical GIS knowledge<\/h3>\n\n

Viewshed analysis is already well known by GIS experts and some tools like QGIS can help you performing it.<\/p>\n\n

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Figure 2a: Digital Surface Model include topography (Digital Terrain Model) and every obstacle on the ground<\/figcaption><\/figure>\n<\/div>\n\n\n\n
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Figure 2b: Viewshed analysis computes all areas visible from an observer (viewpoint)<\/figcaption><\/figure>\n<\/div>\n<\/div>\n\n

However, it requires to have access to local a Digital Surface Model<\/a> (DSM) – a 3D representation of terrain elevation and overlaying objects – and to manually specify the position of all potential observation points.<\/p>\n\n

On Figure 3a below we can see two different types of elevations:<\/p>\n\n