Location intelligence helps governments build a sustainable future
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Take any map and pick a point — you can instantly know a handful of facts about that point. What city or town is it in? Approximately how far is it from any other point on the map? What’s the closest intersection? But suppose you need details beyond the map’s visual scope. That’s where location intelligence comes in — a fusion of data sources that provide a complete picture of any given location, far beyond what’s immediately apparent.
Consider a fire hydrant, for example. Other data that can be tied to the basic geospatial location of the hydrant might be its operating manual, historic and current inspections, images of that hydrant and its surroundings over the years, what the ground surface looked like before it was installed versus after, and more.
Location intelligence is a well-established concept, but incredible advancements in geospatial artificial intelligence (GeoAI) — the fusion of cutting-edge AI with geospatial data and science — are transforming the field. The past couple of decades alone have seen an evolution from digitized hand-drawn maps to high-resolution aerial images, GeoAI-driven analysis, and automated feature extraction. These new tools bring greater efficiency, insights, and value to location intelligence information.
State and local governments depend on location intelligence for many functions and services. As government leaders leverage increasingly advanced geospatial technology to support data-driven decision-making, they need the right tools, imagery, and intelligence to navigate their surroundings and map out efficient, sustainable communities.
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Nearmap MapBrowser showing project progress by comparing imagery from two different dates
The four Rs of location intelligence
Recency
How often is data about any given location collected? Presently, many states and localities might only procure an aerial capture once every five years. If leaders consult the most recent aerial capture in year three out of five, it could lack significant changes to structures, roadways, vegetation, and more.Relevance
Which datasets serve a purpose for a particular question or problem? Not all types of data collected about a location are relevant to all decisions about that location. For example, seasonal tree canopy coverage has a big impact on questions about solar panel installation, but not so much on sidewalk repair.Relation
How do different pieces of data fit together? What are the relationships between datasets? More recently and frequently collected data enables more accurate decision-making.Revelation
What conclusions can be gleaned from the location intelligence story that the data has created? What trends or patterns are apparent, and where are there anomalies or outliers that may impact the decisions government leaders make moving forward?Citywide Pavement Management GeoAI Solution from a Nearmap AI layer of asphalt damage detection and paved areas in ArcGIS Online dashboard
Optimizing the four Rs begins with frequent aerial image capture. Nearmap, a leading location intelligence and aerial technology company, covers 87% of the U.S. population one to three times per year. Having access to multiple high-resolution images of the same location annually — leaf on and leaf off, for example — can help state and local governments closely track how that location is changing.
In addition to frequency, the image quality has significantly advanced since the 3-meter-plus resolution of the first aerial image providers. Today, modern camera systems offer 2- to 3-inch resolution, and both orthographic and oblique views, facilitating the collection of 3D data and creation of AI feature layers.
Game-changing four-R AI applications
Like most of the technology space, AI is revolutionizing location intelligence. Using the 130+ AI feature layers of Nearmap, powered by advanced machine learning models, state and local governments can create digital twins, or virtual representations, of their locales to help them detect patterns and anomalies and enhance their insights. AI can also automate feature extraction, further minimizing tedious manual tasks.
Many cities, for example, are working to increase permeable surfaces due to the negative environmental impact of surface water runoff. By implementing a rating system to identify which properties have a surplus of impermeable surfaces, city leadership can determine how to mitigate via remediation or additional taxation.
AI feature layers can use the most recent data to display properties overlaid with the relevant maps of impermeable surfaces (like driveways or sidewalks) and permeable surfaces (like grass or gravel). City analysts can calculate percentages of impermeable to permeable surfaces to establish the relationship.
Finally, the data-driven revelation they can create is a scale for determining which percentages of impermeable/permeable surfaces are acceptable and identifying the threshold for remediation.
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Citywide Impervious Surface Solution from Nearmap GeoAI in ArcGIS Online Dashboard
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Surface Permeability (desktop view in Esri ArcGIS Pro)
Clean energy and environmental concerns are also top of mind for many cities and states. GeoAI-driven location intelligence applications are becoming powerful tools for city planners when it comes to essential tasks like evaluating spaces for solar panels and electric vehicle (EV) charging stations.
Government analysts can leverage aerial imagery, AI feature layers, and government information like zoning boundaries or tall structure permits to rule out locations too shaded by tall buildings or vegetation to be effective for solar panels. This fusion of government and industry geospatial data enables leaders to develop location-based questions and quickly find answers.
Speed becomes especially important during emergencies, and armed with powerful location intelligence, emergency responders can arrive more prepared than ever. In the event of a disaster, police and emergency responders can consult 3D models to gain situational awareness before arriving on the scene. What buildings are on the scene? What are some landscape details? In the case of a fire, what does the roof of the structure look like and what is it made out of? Arriving with answers to these questions and more enhances the safety of first responders and emergency victims.
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The future of location intelligence
Growing use of GeoAI solutions and feature extraction promises increasingly fast and automated data processing power, while generative AI is poised to make the “revelation” process easier than ever. Picture analysts being able to ask in natural language, “Show me a map of the areas impacted by flooding during the recent hurricane and calculate a total number of affected households.”
It’s just the beginning of the AI revolution in location intelligence, and Nearmap brings to it a combination of depth of experience and continuous exploration. Its geospatial experts have decades of experience in the field, while image capture, data science, and product teams are constantly developing new ways to incorporate cutting-edge technologies. These efforts add up to maximizing the four Rs and streamlining the path of turning imagery into location intelligence and actionable insights.
Learn more about how Nearmap is transforming the fields of aerial imagery and location intelligence for state and local government.