Morning — Why EO Matters & Fundamentals¶

Day 1 · 09:30–12:30 · Module 1 · Sessions 1 & 2

Session 1 — Why Geoinformatics Matters (09:30–10:30)¶

Slide deck: Day 1 Deck 1 — Why Geoinformatics Matters

The spatial evidence gap¶

Dr Sajid opened by setting the scene: IsDB finances projects across dozens of member countries every year — agriculture schemes, water networks, road corridors, schools, health centres. Every project has a location. Almost none has a spatial baseline in its appraisal document.

"Think of the last project report you read. Did it include a single map?" — Dr Sajid Pareeth

Without a spatial baseline, IsDB cannot:

• Verify that a site was well chosen
• Monitor physical change remotely during implementation
• Produce comparable evidence at completion
• Evaluate independently what actually changed

How peer development banks are moving¶

ESA Geodata for Development (GDA) — 110+ EO case studies across 72 countries since 2021. Topics: climate risk, land/water use, infrastructure. An open MOOC provides ready-made use cases. See the ESA GDA tutorials.

IFAD — geospatial strategy 2022–2025 mandates spatial baselines in all new rural investment projects. EO-derived indicators (ETa, LULC) used as outcome variables in impact assessments.

World Bank — Global Environmental Monitoring System (GEMS) and nighttime-lights data track electrification, urban growth, and conflict impact at country scale, cited in every Country Partnership Framework.

The demand signal from IsDB staff¶

106 staff registered for Batch 1 before a single training date was announced:

Registration spanned operations, evaluation, climate, IT, and management — Regional Hubs (34), CCD (11), ESID (10), IEvD (9), STF (7).

Session 2 — Fundamentals of Earth Observation & Geoinformatics (11:00–12:30)¶

Slide deck: Day 1 Deck 2 — EO Fundamentals

What is Earth Observation?¶

Earth Observation (EO) means gathering information about the Earth's surface from a distance — using satellites, aircraft, or drones. The basic physics: the sun's energy reflects off the surface, sensors measure what comes back, and different surfaces return different signals.

A satellite image is not a photograph. It is a grid of pixels, each carrying a measured value. Bands beyond red-green-blue — near-infrared, shortwave infrared — reveal vegetation health, soil moisture, and surface temperature.

Types of satellite data¶

Geoinformatics and spatial data types¶

Geoinformatics is the discipline of collecting, storing, analysing, and presenting location-based information. Spatial data comes in two forms:

• Vector — discrete features with locations:

  • Points — a well, a school, a clinic
  • Lines — a road, a canal, a pipeline
  • Polygons — a project boundary, an irrigation command area, a catchment

• Raster — a grid of values covering an area (a satellite image, a rainfall map, an elevation model)

The three resolutions¶

Every EO dataset involves three trade-offs:

Spatializing an IsDB project¶

"Spatializing" a project means converting the text description of where it is into actual spatial data — so it can be screened, monitored, and evaluated with EO.

flowchart LR
    A["Text description\n'near Diwadi, north of the river'"] --> B[Coordinates\nlat/lon]
    B --> C["Boundary / Line / Point\n(KML, GeoJSON, Shapefile)"]
    C --> D["Spatial layers\nETa, water, LULC, hazard"]
    D --> E["Evidence for project documents\nPCN → PAD → PIASR → PCR → PPER"]

Once spatial, a project can establish a baseline, get standard maps in its PCN/PAD/RRM, be monitored remotely during implementation, and have its outcomes verified at completion — all using the same boundary.

Continue to Afternoon — Mainstreaming EO & Exercise 1