Clarity and confidence

How high-resolution satellite imagery can help you extract critical information at scale

Spatial resolution: distance and detail

As the most commonly used metric for classifying optical satellite imagery, spatial resolution refers to the distance represented by a pixel in an image. For example, NASA’s Landsat collects imagery at 15-meter resolution—so every pixel in one of its images represents a 15 m by 15 m square on the ground.

Most commercial imagery falls between 2 and 5 meter resolution, with high-resolution sensors capturing at 70, 50 and 30 centimeter resolution. Each increase in resolution results in an exponential increase in the amount of critical information held in each pixel.

The advantage of satellite imagery is having a unique perspective over your area of interest. But not all imagery is equally useful. Low and medium resolution imagery enables you to see shapes, colors and large environmental bodies like water or forests. High spatial resolution reveals smaller features such as vehicles, buildings and even people details that impact critical decisions.

Quantifying your environment

Whether you’re mapping a dense urban landscape or remote village, 30 cm satellite imagery offers a cost-effective way to detect objects at scale and develop valuable and diverse datasets.

City planners and autonomous vehicle (AV) engineers alike need to know where traffic lanes are, as well as bike lanes, crosswalks, parking lots, etc. A retail business might consider this information when choosing a new location. Whatever your aim, high spatial resolution satellite imagery has the clarity to identify and extract small features unseen in low-resolution images.

Assessing damage: Hurricane Dorian

When a natural disaster hits, response and relief organizations need information on impacted areas, fast. How many people live in that area? How can we reach them? Are the buildings and roads damaged? High spatial resolution satellite imagery can answer these questions, helping teams mobilize resources quickly and effectively.

Once the initial response is underway, insurance organizations and others need reliable damage assessments for claims and recovery efforts. Machine learning algorithms and crowdsourcing can help identify damage and destroyed infrastructure, but these methods are only truly reliable if they run on high-resolution data that clearly shows these features on a large scale.

Monitoring activity: national security

In low or medium resolution imagery, you can see landscapes, building outlines, and shapes and colors that indicate something is there. But if you need to know exactly what is there, you need higher resolution data. In matters of national security, high-resolution imagery reveals, for example, if vehicles are farming or military equipment. It also enables analysts to identify people on the ground.

Whether you're monitoring your borders and adversary activities or looking to identify weapons systems for potential treaty or sanction violations, this level of detail is crucial for enhanced situational awareness and informed actions.

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