Understand the Language of Earth Observation

Imagery captured from crewed aircraft flying below satellite altitude. It typically offers very high spatial resolution over a defined area and is well suited to detailed mapping and inspection of cities, sites, and infrastructure.

The fraction of incoming sunlight a surface reflects, from 0 (fully absorbing) to 1 (fully reflecting). Snow and ice have high albedo; forests and asphalt have low albedo — a useful signal in climate, energy, and land-cover analysis.

Satellite data that has already been corrected, calibrated, and standardized — typically georeferenced, atmospherically corrected, and tiled — so analysts can use it immediately without heavy pre-processing.

The specific geographic area you want imagery for, usually drawn as a polygon or bounding box. Your AOI defines what gets searched, priced, tasked, and delivered.

The process of removing the effects of the atmosphere — haze, scattering, and absorption — from raw imagery, so that pixel values reflect conditions on the ground rather than the air in between. Essential for comparing imagery over time.

A discrete range of wavelengths a sensor records, such as red, green, blue, or near-infrared. Combining bands in different ways produces natural-color views, false-color composites, and spectral indices.

The reference layer of imagery or cartography that other geospatial data is drawn on top of. It provides geographic context — roads, terrain, or recent satellite imagery — without being the primary subject of analysis.

The measurement of water depth and the shape of underwater terrain. Satellite-derived bathymetry uses how light penetrates and reflects through shallow water to map coastlines, reefs, and seabeds.

Comparing imagery of the same location captured at different times to identify what has changed — new construction, deforestation, flooding, or encroachment. One of the highest-value workflows in Earth observation.

The percentage of an image obscured by clouds. High cloud cover can make optical imagery unusable, which is why analysts filter by it — and why cloud-penetrating radar (SAR) matters in persistently cloudy regions.

The framework that ties coordinates in your data to real positions on Earth, defining the datum, projection, and units. Two datasets must share a CRS — or be reprojected to one — before they can be accurately overlaid.

A 3D representation of terrain elevation across an area, stored as a grid of height values. DEMs underpin orthorectification, flood modeling, line-of-sight analysis, and slope and viewshed calculations.

An elevation model that includes everything on the surface — buildings, vegetation, and structures — in addition to the bare ground. Useful for measuring heights of objects and modeling line of sight.

An elevation model of the bare earth with surface features like buildings and trees removed. DTMs are used for hydrology, engineering, and terrain analysis where the underlying ground matters.

Imagery collected by uncrewed aerial vehicles flying at low altitude. It delivers extremely high resolution over small areas on demand, ideal for close inspection, surveying, and progress monitoring of individual sites.

The collection of information about Earth's physical, chemical, and biological systems using remote sensing — primarily satellites, aircraft, and drones. The umbrella term for the field SkyWatch operates in.

The full range of energy wavelengths, from radio waves to gamma rays. Remote sensing instruments record specific portions of it — visible light, infrared, microwave — and each portion reveals different things about the surface.

An image that maps non-visible bands (such as near-infrared) to visible colors to highlight features the eye can't normally see. A common false-color view renders healthy vegetation in bright red to make plant health obvious.

Automatically identifying and outlining objects of interest in imagery — buildings, roads, water bodies, vehicles — to turn raw pixels into structured, mappable data.

The area on the ground covered by a single image or scene. Comparing an image's footprint to your AOI tells you how much of your target area a given capture actually contains.

Software for storing, analyzing, visualizing, and managing spatial data. Platforms like ArcGIS let teams combine imagery with other layers — parcels, assets, sensors — to answer location-based questions.

Assigning real-world coordinates to an image so each pixel maps to a known location on Earth. Without it, imagery can't be measured, overlaid, or compared accurately against other data.

A widely used raster image format that embeds georeferencing information — coordinate system and position — directly in the file, so imagery opens in the right place in any GIS without a separate world file.

A location whose precise coordinates are known on the ground and identifiable in an image. GCPs are used to georeference and orthorectify imagery, improving positional accuracy.

The real-world distance represented by one pixel in an image — for example, 30 cm GSD means each pixel covers a 30 cm square on the ground. Smaller GSD means finer detail. Often used interchangeably with spatial resolution.

Imaging that captures hundreds of narrow, contiguous spectral bands, producing a detailed spectral fingerprint for every pixel. It can distinguish materials and minerals that look identical in ordinary color imagery.

The angle at which a sensor views a target relative to straight down (nadir). Larger off-nadir angles let satellites see to the side and revisit faster, but can distort geometry and stretch ground resolution.

A radar technique that compares the phase of two or more SAR images of the same area to measure tiny ground movements — millimeters of subsidence, uplift, or structural shift — over time.

Light Detection and Ranging — an active sensor that fires laser pulses and times their return to build precise 3D point clouds of terrain, structures, and vegetation, even penetrating tree canopy to map the ground beneath.

The descriptive information packaged with an image: when and where it was captured, by which sensor, at what resolution, cloud cover, sun angle, and more. Metadata is how you find, filter, and trust imagery.

A single seamless image assembled by stitching together multiple overlapping scenes. Mosaics provide continuous coverage of an area too large for one capture.

Imaging that records a handful of broad spectral bands — typically red, green, blue, and near-infrared. It supports natural-color views plus vegetation, water, and land-cover analysis, and is the workhorse of optical EO.

The point on the ground directly beneath a sensor. A nadir view looks straight down, giving the truest geometry and least distortion; off-nadir views look to the side.

Normalized Difference Vegetation Index — a ratio of near-infrared to red reflectance that measures vegetation health and density. Values near 1 indicate dense, healthy vegetation; low values indicate bare soil, water, or stress.

Imagery formed from reflected sunlight in the visible and infrared bands — the familiar 'photographic' satellite view. It is intuitive and information-rich but depends on daylight and clear skies.

The path a satellite follows around Earth. Earth-observation satellites usually fly in Low Earth Orbit (LEO) for detail, with the specific orbit determining coverage, revisit frequency, and lighting conditions.

Correcting an image for terrain relief, sensor tilt, and viewing geometry so that every pixel sits in its true map position. Orthorectified imagery can be measured and overlaid like a map.

A single-band image that records overall brightness across a wide range of visible light, usually at higher spatial resolution than a sensor's color bands. Often fused with color bands via pan-sharpening.

Merging a high-resolution panchromatic band with lower-resolution color bands to produce a single image that is both sharp and in color, combining the best of each.

The smallest element of a digital image. In remote sensing, each pixel represents a measured patch of ground; how much ground it covers is the image's ground sample distance.

A set of 3D points, each with a position and often color or intensity, that together describe the shape of surfaces and objects. Produced by LiDAR and by photogrammetry from overlapping imagery.

How finely a sensor distinguishes differences in energy or brightness, expressed in bits. Higher radiometric resolution (e.g. 12-bit vs 8-bit) captures subtler tonal variation and detail in shadows and highlights.

Data stored as a grid of cells or pixels, each holding a value — the structure of all imagery and continuous surfaces like elevation. Contrast with vector data, which stores discrete points, lines, and polygons.

Gathering information about an object or area from a distance — without physical contact — typically by measuring reflected or emitted energy from satellites, aircraft, or drones. The core method behind Earth observation.

Recalculating pixel values when an image is reprojected, resized, or aligned to a new grid. The method chosen (nearest-neighbor, bilinear, cubic) affects the sharpness and accuracy of the result.

How often a satellite or constellation can image the same location. Shorter revisit times mean fresher data and finer change detection — daily, or even multiple times per day with large constellations.

The level of ground detail an image captures, defined by the size of the smallest distinguishable feature — closely tied to ground sample distance. Higher spatial resolution reveals smaller objects.

The number and narrowness of the spectral bands a sensor records. Higher spectral resolution — as in hyperspectral imaging — lets you distinguish materials with similar appearance but different spectral signatures.

The characteristic pattern of how a material reflects and absorbs energy across wavelengths. Because water, vegetation, soil, and minerals each have distinct signatures, spectral data can identify what's on the ground.

A near-polar orbit timed so the satellite passes over each location at roughly the same local sun time on every revisit. Consistent lighting makes images far easier to compare across days and seasons.

Sorting image pixels into land-cover classes using training samples an analyst has labeled. The algorithm learns from the examples, then assigns every pixel to the most similar class.

The width of the strip of ground a sensor images in a single pass. Wider swaths cover more area per orbit, supporting broad monitoring; narrow swaths often trade coverage for higher resolution.

An active radar sensor that emits microwave pulses and measures their echo, building imagery independent of sunlight and able to see through clouds, smoke, and darkness — invaluable for all-weather, day-or-night monitoring.

Commissioning a satellite to capture new imagery of a specific location at a future time, rather than buying from the existing archive. Tasking gets you fresh data of exactly the area and window you need.

How frequently imagery of a location is available over time — essentially the cadence of revisit. High temporal resolution is what makes monitoring and timely change detection possible.

The portion of the infrared spectrum tied to heat emitted by surfaces. Thermal imagery reveals temperature differences — useful for detecting fires, equipment activity, water stress, and urban heat.

An image that combines the red, green, and blue bands to show a scene roughly as the human eye would see it from above. The most intuitive way to view optical imagery.

Grouping image pixels into clusters by statistical similarity without pre-labeled training data. The analyst interprets and labels the resulting clusters afterward — useful when ground truth is scarce.

Spatial data represented as discrete geometry — points, lines, and polygons — with attached attributes, such as building outlines or road networks. The complement to raster data in a GIS.

Open standards for streaming map imagery over the web as rendered tiles or images, so you can pull live layers straight into a GIS or web app without downloading and hosting the full dataset.