Relief
We start from elevation models and mapping data with the source documented.
Products Relieva
Cities and 3D models Prototype & research
A city you can explore in depth.
We develop 3D city representations that combine relief, maps and data about the built space. The Relieva direction is updating the model through field observations, guided by the areas where changes appear.
What the layers represent
Relief provides the base, buildings describe the built space, and observations indicate places to check. Turn layers on and off.
Relieva
From information to work done.
Urban model
We are building a navigable representation of buildings, routes and surroundings.
Actualizare
We are researching field data collection and how satellite observations can prioritise revisiting.
Where it becomes useful.
3D exploration
Understanding the relief and the relationship between buildings, roads and territory.
Planificare
Demonstrations for routes, observation and documenting changes.
Field research
Prototypes for linking local observations and the digital model.
Relieva is a research and prototyping project. The models may include estimated heights; they do not represent a cadastral survey or a fleet of robots already deployed.
Relieva in detail
What you can do with this project.
Relieva asks a simple question about a city: where something on wheels can go up and where it cannot. Chișinău has 234 metres between its lowest and highest point, and that decides whether an autonomous delivery, a wheelchair or a ramp has a place to exist. The prototype measures this from public data, not from estimates.
The basis is Copernicus DEM GLO-30: two one-degree tiles read from the public AWS Open Data archive, reprojected to UTM zone 35N at 25 m per pixel, with slope calculated using the Horn 3×3 kernel — the same method as the standard tool in GDAL. On top of it are placed the street geometry and building footprints from OpenStreetMap.
Each block on the page carries a tag: measured, from sources or proposed. The distinction is necessary because otherwise all statements would sound the same, and the one that matters to a buyer is exactly the one that says “this does not exist yet”.
Municipal slope map
Analysed area 555 km², urban core 230 km². Distribution across four bands: 21.2% of the core below 4% slope (48.7 km²), 31.0% between 4 and 8% (71.3 km²), 23.4% between 8 and 12% (53.7 km²), 24.5% above 12% (56.3 km²). The thresholds are not chosen for aesthetics: 8.33% is the 1:12 accessibility ramp, and 12% is the threshold above which sidewalk robots rated at 10–15% no longer climb.
The actual slope of the streets, not of the terrain
7,929 street segments with individually calculated slope, median 3.7%. Out of 156 named streets longer than 400 m, only one street exceeds 8% on average. The conclusion overturns intuition: the terrain is rough, the carriageway is not — engineers drew the roads along the contour lines.
The centre in 3D, with height provenance separated
9,506 building footprints from OpenStreetMap (snapshot 02.07.2026), placed on the ground level beneath each one. Of them, 1,128 have the height declared in OSM; the other 8,378 are estimated from the number of floors × 3.2 m. The two categories are coloured differently in the scene, precisely so that measurement is not confused with estimation.
View from a robot’s eyes
A camera 65 cm above the pavement, placed at the real coordinates of a street in Chișinău, oriented along the carriageway, with the real buildings at the real levels. Scale 1:1, without vertical exaggeration — unlike the relief scene, where ×3.5 exaggeration is written under the image.
Sentinel image requested on load
Three live Copernicus layers for a fixed area of Chișinău: Sentinel-2 true colour (10 m), Sentinel-2 bare soil / disturbed surface (10–20 m) and Sentinel-1 GRD VV radar (10 m, IW mode, DV polarisation, with GAMMA0 terrain correction and orthorectification). The window covers the last 30 days.
Altimetric profile of the city
A 22.9 km west–east section through the centre, 300 samples, between 36.0 and 215.4 m. The full model covers 23.4 × 25.05 km, with relief between 24.0 and 257.6 m.
Data and operation
What enters the system. What needs to be checked.
- The trap that matters: GLO-30 is a surface model
- Copernicus DEM GLO-30 is DSM, not DTM — in a dense block it returns roof or tree canopy height, not the carriageway. It gives no error; only the numbers come out wrong. Specifically, when building this project it produced a 13.5% slope on Ştefan cel Mare Boulevard, which is practically flat: two points 30 m apart, one falling on a cornice. For the same reason, roads came out a median 13 m below buildings and disappeared between blocks in the 3D scene.
- What we did with it, and what we chose not to hide
- For 3D geometry, an approximated terrain is used (minimum filter on a 5 px window at 25 m/px, that is 125 m — wider than a block, so buildings stop being terrain, but terraces survive). The published statistics, however, remain on the raw DSM, and the page says so: because the model includes buildings and canopies, in dense blocks the slope comes out higher than it is in reality on the carriageway. The figure of 52.2% of land under 8% slope is therefore a lower bound, not an optimistic one.
- Slope is measured over a long baseline
- Two consecutive vertices from an OpenStreetMap line can be 4 metres apart — below the 25 m raster cell. Distinguishing them means measuring raster noise, not the road. That is why the gradient is calculated over a window that looks forward and backward up to a fixed distance, and returns zero if the base remains below 8 m.
- Measured, sourced, proposed — three different things
- Measured: relief, slope, profile, building geometry on the ground. Sourced: OpenStreetMap footprints and street geometry, with contributor attribution and the ODbL licence. Proposed: the rescan loop through courier fleets, robots, drones, the accessibility map. The rover illustration explains a concept; it is not proof of delivered equipment.
- Copernicus elevation dictates the architecture
- The area of interest of the satellite layer is fixed, not chosen by the visitor — precisely to limit how much of the 30,000 processing units per month allowance traffic can spend. Beyond that, the response is cached for 6 hours at origin and 24 hours in the CDN, with background revalidation for a week. The satellite scene changes every few days anyway, not every few minutes.
- OSM data freshness
- The street geometry is from the 06.05.2026 snapshot, the building footprints from 02.07.2026. OpenStreetMap coverage and freshness differ from one area to another; it is not a cadastral register and cannot be treated as one.
From exploration to implementation
How we prepare a project with Relieva.
1. We choose the territory and the question
A city, a district, a route. The question must be one decided by slope: where a device can move, where a ramp is needed, which route has the least cumulative climb.
2. We check coverage before the offer
Copernicus DEM GLO-30 covers globally at 30 m, but OpenStreetMap coverage and quality vary widely. The density of footprints and street geometry in the requested area is checked before we promise a result.
3. Building the layers from the source
Reprojection in the zone’s UTM, Horn slope 3×3, contour lines, profile, filtered terrain for geometry. For each published figure, the method and the basis on which it was calculated are kept — raw DSM or filtered terrain — because mixing them makes the result inconsistent.
4. A pilot, if it is about field collection
Any image collection in the city starts with what is blurred at source (faces, registration plates), what is kept (derived geometry) and an impact assessment before the first camera is installed. Law 195/2024 on data protection applies from 23 August 2026.
Questions worth clarifying.
Where does the relief come from and how accurate is it?
Copernicus DEM GLO-30, taken from the public AWS Open Data archive — two one-degree tiles, reprojected into UTM 35N at 25 m per pixel. The native resolution is 30 m, so one cell covers about half a block. It sees terraces, slopes and the valley floor; it does not see the kerb, the ramp or the ditch.
Is the elevation model DSM or DTM, and why does it matter?
It is DSM — a surface model. That means that in dense blocks it returns the roof or tree canopy elevation, not the carriageway. It matters because it is a silent trap: nothing errors, only the figures come out wrong. It gave us 13.5% slope on Bulevardul Ştefan cel Mare, which is flat, because one point landed on a cornice. The geometry fix is a minimum filter over 125 m; for the published statistics we chose to stay with raw DSM and say on the page that the 52.2% figure is a lower threshold.
Are building heights measured or guessed?
Of 9,506 buildings, 1,128 have the declared height in OpenStreetMap. The other 8,378 are estimated from the number of storeys multiplied by 3.2 m. In the scene, the two categories have different colours, precisely so it is visible which is which. The ground level under each building comes from the elevation model, not from estimation.
Is it a cadastral map? Can I use it in a file?
No. It does not have the precision or the legal status of an authorised survey — the geometry comes from OpenStreetMap, which is a collaborative map, and the elevation from a global 30 m model. What it offers is context: slope, relief, urban volume, in a form in which planning or service zoning decisions can be made, followed by field verification where it matters.
Do your robots already move through the city?
No. There is no fleet, no installed sensors and no rescanning loop. They are marked “proposed” on the page, alongside the accessibility map. What exists today is the measurement layer from public data and the scene that shows them. A real pilot would require verified hardware, evaluated routes, approved operating conditions and data protection rules established before the first camera.
Is the satellite image real or an illustration?
Real and requested on load. The server requests from Copernicus a Sentinel-2 L2A true colour raster at 10 m for the Chișinău area, over a 30-day window, with a 40% cloud filter, and returns it with headers that say exactly which product it is and which interval it covers. There is also a Sentinel-1 radar layer, which passes through clouds. It is not live imagery: Sentinel-2 returns over the same place every few days.
Can it be done for another city?
The elevation model is global, so the relief and slope part is rebuilt almost identically anywhere. What changes is OpenStreetMap: in cities with few building footprints, the 3D scene and street slope come out thin. Coverage is checked before the offer, not after.
Illustrative example
Where a kerbside delivery service could exist in Chişinău
A use case, without client data or attributed commercial results.
Initial situation
An operator wants to know which sectors are worth starting autonomous device delivery in, and where it is not worth trying.
How it works
The slope is calculated on the urban core from Copernicus DEM GLO-30 (Horn 3×3, 25 m) and classified into four bands at 4%, 8.33% and 12%. Separately, the longitudinal slope of each street segment from OpenStreetMap is calculated on a long basis, so that raster noise is not measured. The two layers are overlaid.
Rezultatul
52.2% of the urban core lies below 8% slope, but the useful result is not the percentage — it is its shape. The difficult areas gather on the slopes between terraces, while the plateaus and the valley floor remain usable. This turns “52%” into a zoning plan: service areas are drawn on the terrace, not on a kilometre radius. And, separately, the carriageway is much gentler than the terrain — the median street slope is 3.7%, and of 156 named streets over 400 m only one exceeds 8% on average.
What is required:An OpenStreetMap snapshot with decent coverage of street geometry in the targeted area. The result is a feasibility map, not a certified route: any route for an autonomous device must be verified in the field, because the model does not see kerbs, ramps or obstacles.
Collaboration possibilities
Relieva, in the context of your organisation.
Planning in a geographical context
Geographical models and layers for exploring the territory and discussing scenarios. Estimated data are distinguished from verified measurements.
Private companies
We define a pilot around a real process: users, data, integrations, costs and acceptance criteria. Expansion follows after evaluating the result.
State institutions and companies
We establish the requirements for accessibility, hosting, data protection and interoperability. Any connection to AGE or STISC services requires validation of eligibility, access and approvals.
These are adaptation scenarios, not statements about existing contracts or partnerships. The proposed functions are confirmed within the project scope.
Discuss a pilotWe also look beyond the map.
See how we use Copernicus observations and how we explore satellite orbits.
Part of an ecosystem.
Geospatial intelligence
FlowMind
FlowMind organises maps, public sources and Copernicus products into a single analysis workspace.
Specialised deploymentCivic platform
sesizari.md
sesizari.md organises reports about public space: location, category, description and the relevant material.
Public platformVisual interaction & agents
World Agent
World Agent is a demonstrator for natural-language interaction with a visual scene.
Research demonstratorWhat would you want to work better?
Tell us about your process. Together we decide what is worth building, what we can connect, and how we check the result.