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Kharita Challenges

Problems

Real geospatial problems, graded automatically. Choose a track, a level and what you want to hand in.

3 of 59 problems

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Track
Spatial Analysis8 problemsPostGIS / SQL0 problemsPython0 problemsRemote Sensing3 problemsCartography1 problemWeb GIS0 problemsData Management8 problemsGIS Architecture1 problem
Level
Beginner0 problemsIntermediate2 problemsAdvanced3 problemsExpert0 problems
Hand in
SQL0 problemsPython0 problemsGeoJSON3 problemsAnswers2 problemsSchema0 problemsDecisions1 problemDataset file2 problems
Category
Data preparation0 problemsData quality0 problemsSpatial analysis0 problemsRaster analysis2 problemsCartography0 problemsDecision support1 problemGeospatial systems0 problemsWeb delivery0 problems
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Remote SensingAdvancedGeoJSONClear all
  1. Not started, Defend a borough greening shortlistProjectDecision support3 steps

    Measure canopy and summer vegetation on their own grids, then add low-ground context and apply one transparent shortlist rule.

    Remote SensingadvancedDifficulty: advanced620 XP~110 min13 checks

    Worked in 3 steps, each checked before the next one opens.

    1. Audit the vegetation rasters · Answers
    2. Deliver the borough evidence table · Dataset file
    3. Add low-ground context and shortlist · GeoJSON

    Datasets

    • Greater London land cover 2021 (ESA WorldCover 10 m) · Raster · EPSG:4326
    • Greater London, Sentinel-2 red and NIR, 11 July 2025 · Raster · EPSG:32630
    • Greater London elevation (Copernicus DEM GLO-30) · Raster · EPSG:4326
    • London boroughs · Polygon · 33 features · EPSG:4326

    What you’ll do

    • Audit the two vegetation measures from observed raster pixels
    • Hand over a per-borough canopy and NDVI table as a GIS file
    • Add area-weighted low ground and an explicit shortlist flag
    zonal statisticsNDVIland coverDEMmulti-criteriaLondonAlso trains Spatial Analysis, Data Management
    Open project
  2. Not started, Mean NDVI per borough, across a projection boundaryRaster analysisGeoJSON

    Zonal statistics where the raster is in UTM and the polygons are in WGS 84: reproject the right layer, compute the index per borough, and hand back a map.

    Remote SensingadvancedDifficulty: advanced300 XP~50 min7 checks

    You hand in a GeoJSON result.

    Datasets

    • Greater London, Sentinel-2 red and NIR, 11 July 2025 · Raster · EPSG:32630
    • London boroughs · Polygon · 33 features · EPSG:4326

    What you’ll do

    • Recognise a CRS mismatch between a raster and a vector layer
    • Reproject the vector layer rather than resample the raster
    • Compute a per-zone mean of a derived index
    NDVIzonal statisticsCRSUTMSentinel-2LondonAlso trains Spatial Analysis, Data Management
    Open problem
  3. Not started, Screen Newham’s greening gridProjectRaster analysis3 steps

    Audit a real land-cover chip, deliver a measured grid file, and identify cells for field verification.

    Remote SensingadvancedDifficulty: advanced600 XP~100 min10 checks

    Worked in 3 steps, each checked before the next one opens.

    1. Audit the class chip · Answers
    2. Hand over the measured grid · Dataset file
    3. Map field-verification cells · GeoJSON

    Datasets

    • Newham WorldCover 2021 class chip · Raster · EPSG:4326
    • London boroughs · Polygon · 33 features · EPSG:4326

    What you’ll do

    • Audit class counts in the clipped raster
    • Prepare a traceable borough grid as an uploaded GIS file
    • Return only cells that meet a stated greening screen
    NewhamWorldCoverurban greeningoffline rasterAlso trains Data Management, Spatial Analysis
    Open project