Geoinformatics: The Science, Technology, and Future of Spatial Intelligence

Introduction

Geoinformatics has emerged as one of the most important interdisciplinary fields for understanding, managing, analyzing, and visualizing information associated with the Earth's surface and its changing environments. By integrating Geographic Information Systems (GIS), remote sensing, Global Navigation Satellite Systems (GNSS), surveying, photogrammetry, spatial databases, cartography, geospatial artificial intelligence, and related computational technologies, geoinformatics transforms geographically referenced data into actionable information.

Today, location is an essential dimension of decision-making. Governments use geospatial information to manage cities, infrastructure, land resources, and emergency response. Environmental organizations use satellite imagery and spatial models to monitor forests, water resources, biodiversity, and climate-related changes. Businesses use location intelligence to understand markets, optimize logistics, select facilities, and analyze customer behavior. Engineers, surveyors, planners, researchers, and scientists increasingly rely on geospatial technologies to measure and interpret the physical world.

The growing availability of Earth-observation satellites, unmanned aerial vehicles (UAVs), GNSS receivers, mobile mapping systems, LiDAR sensors, cloud computing platforms, and artificial intelligence has dramatically expanded the capabilities of geoinformatics. What was once largely associated with digital mapping has developed into a sophisticated ecosystem for spatial data acquisition, processing, analysis, modeling, visualization, and decision support.

This article explores the foundations of geoinformatics, its major technologies, applications, benefits, challenges, emerging trends, and future potential.

What Is Geoinformatics?

Geoinformatics is an interdisciplinary scientific and technological field concerned with the acquisition, management, processing, analysis, modeling, visualization, and dissemination of geospatial information.

The term combines the concepts of geography and informatics. Geography provides the spatial perspective, while informatics provides computational methods for handling information. Geoinformatics therefore focuses on understanding phenomena through their geographic location, spatial relationships, patterns, and changes over time.

A useful way to conceptualize geoinformatics is as a complete information lifecycle:

Data acquisition → Data processing → Data management → Spatial analysis → Modeling → Visualization → Decision support

This lifecycle encompasses numerous technologies and scientific disciplines.

Geoinformatics is broader than GIS alone. A GIS is a central component of geoinformatics, but the wider discipline also includes satellite remote sensing, GNSS positioning, photogrammetry, surveying, LiDAR, spatial databases, web mapping, geospatial programming, spatial statistics, and increasingly machine learning and artificial intelligence.

The fundamental objective is not merely to create maps. It is to convert spatial observations into reliable knowledge that can support scientific research, operational management, planning, and decision-making.

The Core Components of Geoinformatics

Geoinformatics consists of several interconnected domains. Each contributes specialized capabilities to the geospatial information lifecycle.

1. Geographic Information Systems

Geographic Information Systems are among the most recognizable components of geoinformatics.

A GIS is a computer-based environment for capturing, storing, editing, querying, analyzing, managing, and displaying geographically referenced information.

GIS generally works with two fundamental types of spatial data:

Vector Data

Vector data represents geographic features using:

• Points

• Lines

• Polygons

A point may represent a survey marker, well, tree, or utility pole. A line may represent a road, river, pipeline, or railway. A polygon can represent a parcel, lake, administrative boundary, forest, or land-use zone.

Vector features are particularly useful when geographic objects have clearly defined boundaries.

Raster Data

Raster data represents geographic information as a grid of cells or pixels. Each cell contains a value representing a phenomenon at a particular location.

Raster datasets include:

• Satellite imagery

• Digital elevation models

• Temperature surfaces

• Precipitation grids

• Land-cover classifications

• Soil maps

• Population density surfaces

Raster analysis is particularly important for continuous geographic phenomena and Earth-observation applications.

Modern GIS platforms can integrate both vector and raster datasets, allowing users to conduct complex spatial analyses.

2. Remote Sensing

Remote sensing is another fundamental pillar of geoinformatics.

Remote sensing involves collecting information about an object, area, or phenomenon without direct physical contact. Sensors mounted on satellites, aircraft, drones, or other platforms detect electromagnetic energy reflected or emitted from Earth's surface.

Satellite remote sensing has transformed geospatial science by providing observations over large geographic areas at repeated intervals.

Common remote sensing technologies include:

• Optical satellite imagery

• Multispectral imaging

• Hyperspectral imaging

• Synthetic Aperture Radar (SAR)

• Thermal infrared sensing

• LiDAR

• Radar altimetry

Remote sensing supports applications ranging from agricultural monitoring to disaster assessment and climate research.

For example, vegetation indices derived from multispectral imagery can help identify vegetation condition. Radar imagery can provide information under cloudy conditions and can be used for applications such as flood mapping, deformation monitoring, and land-surface analysis.

3. Global Navigation Satellite Systems

GNSS technology provides precise positioning and timing information anywhere satellite signals can be received.

Although GPS is the best-known GNSS, the broader GNSS ecosystem includes systems such as:

• GPS

• GLONASS

• Galileo

• BeiDou

Modern receivers can combine signals from multiple constellations to improve positioning availability and performance.

GNSS is used extensively in:

• Land surveying

• Construction

• Precision agriculture

• Navigation

• Mapping

• Transportation

• Geodetic monitoring

• UAV operations

• Infrastructure development

High-precision techniques such as Real-Time Kinematic (RTK) and Precise Point Positioning (PPP) can achieve centimeter-level positioning under appropriate conditions and processing configurations.

GNSS therefore provides a critical geospatial reference framework for many geoinformatics applications.

4. Surveying and Geodesy

Surveying provides the measurement foundation for geospatial information.

Traditional surveying methods include:

• Total stations

• Leveling instruments

• Theodolites

• Electronic distance measurement

• GNSS surveying

Modern surveying increasingly combines these technologies with digital workflows.

Geodesy, meanwhile, deals with the measurement and representation of Earth's geometry, orientation, gravity field, and temporal changes.

Geodesy is essential because spatial data must be referenced to an appropriate coordinate system and geodetic datum.

Errors in coordinate reference systems, datums, transformations, or measurement procedures can propagate through an entire geospatial project. Consequently, understanding geodetic principles is fundamental for high-quality geoinformatics.

5. Photogrammetry

Photogrammetry is the science and technology of obtaining reliable geometric information from photographs and imagery.

Modern digital photogrammetry can generate:

• Orthophotos

• Point clouds

• Digital Surface Models

• Digital Terrain Models

• Three-dimensional meshes

• Three-dimensional reconstructions

Unmanned aerial vehicles have significantly increased access to photogrammetric data acquisition.

A drone equipped with a calibrated camera can capture overlapping imagery over an area of interest. Photogrammetric software can then reconstruct the scene using image-matching and structure-from-motion techniques.

This workflow is widely used in:

• Topographic mapping

• Construction monitoring

• Mining

• Agriculture

• Archaeology

• Corridor mapping

• Environmental surveys

• Infrastructure inspection

6. LiDAR and Three-Dimensional Geoinformatics

Light Detection and Ranging, commonly known as LiDAR, uses laser pulses to measure distances between a sensor and surrounding surfaces.

LiDAR systems can be mounted on:

• Aircraft

• UAVs

• Vehicles

• Tripods

• Mobile mapping systems

• Satellites

The resulting point clouds can contain millions or billions of three-dimensional observations.

LiDAR is particularly valuable for:

• Terrain modeling

• Forest structure analysis

• Urban modeling

• Flood modeling

• Power-line mapping

• Transportation infrastructure

• Building extraction

• Coastal monitoring

The growing importance of three-dimensional geospatial information is driving geoinformatics beyond conventional two-dimensional mapping toward detailed digital representations of physical environments.

Spatial Data: The Foundation of Geoinformatics

The effectiveness of any geoinformatics project depends heavily on data quality.

Spatial data generally contains two essential components:

1. Geometry — where an object or phenomenon is located.

2. Attributes — information describing that object or phenomenon.

Temporal information is increasingly important as well. A modern geospatial dataset may therefore be considered multidimensional, containing location, attributes, and time.

For example, a road dataset could contain:

• Geographic coordinates

• Road classification

• Surface type

• Width

• Speed limit

• Traffic volume

• Construction date

• Maintenance history

When such information is combined with spatial relationships, GIS can answer complex questions.

For example:

Which roads are within 500 meters of schools located in areas with high population density and elevated traffic volume?

This illustrates the analytical power of geoinformatics: multiple datasets can be integrated based on their spatial relationships.

Coordinate Reference Systems and Spatial Accuracy

One of the most technically important concepts in geoinformatics is the coordinate reference system (CRS).

A CRS defines how geographic coordinates relate to positions on Earth.

Geographic coordinate systems commonly use latitude and longitude, whereas projected coordinate systems transform Earth's curved surface into a planar representation suitable for specific applications.

Common concepts include:

• Geographic coordinates

• Projected coordinates

• Datums

• Map projections

• Ellipsoids

• Coordinate transformations

• Georeferencing

Selecting an inappropriate CRS can introduce distortions or positional inconsistencies.

For professional geospatial projects, coordinate system management is therefore not an administrative detail; it is a fundamental component of spatial accuracy.

Spatial Databases and Geospatial Data Management

The volume of geospatial data has increased dramatically.

Satellite constellations continuously collect imagery. Mobile devices generate location information. Sensors record environmental conditions. Cities deploy Internet of Things infrastructure. Drones collect high-resolution imagery. Surveying projects generate large point clouds.

Traditional file-based data management can become insufficient at scale.

Spatial databases address this problem by providing database structures and functions specifically designed to manage geographic information.

A spatial database can support:

• Spatial indexing

• Geometry storage

• Spatial queries

• Topological relationships

• Attribute management

• Multi-user access

• Transaction management

• Large-scale data processing

Spatial database technologies are increasingly integrated with cloud infrastructure and distributed computing environments.

Spatial Analysis

Spatial analysis is one of the defining capabilities of geoinformatics.

Unlike conventional database queries, spatial analysis considers geographic relationships.

Typical spatial operations include:

• Buffering

• Overlay analysis

• Proximity analysis

• Spatial joins

• Network analysis

• Terrain analysis

• Interpolation

• Density analysis

• Hotspot analysis

• Suitability modeling

• Visibility analysis

For example, a municipality might identify suitable locations for a new hospital by combining:

• Population distribution

• Road accessibility

• Existing healthcare facilities

• Land ownership

• Flood risk

• Slope

• Utility availability

The resulting suitability model can support transparent and evidence-based planning.

Geoinformatics and Artificial Intelligence

Artificial intelligence is rapidly changing the geospatial sector.

Traditional geospatial workflows often required specialists to manually interpret large datasets. Machine learning can automate many classification and prediction tasks.

Applications include:

• Land-cover classification

• Building detection

• Road extraction

• Crop classification

• Object detection

• Change detection

• Flood mapping

• Wildfire assessment

• Urban expansion monitoring

• Image segmentation

Deep learning techniques, particularly convolutional neural networks and transformer-based architectures, can process large volumes of satellite and aerial imagery.

However, AI does not eliminate the need for geospatial expertise.

Model performance depends on factors such as:

• Training-data quality

• Spatial resolution

• Sensor characteristics

• Geographic transferability

• Class imbalance

• Label accuracy

• Temporal variability

• Validation methodology

A model trained in one geographic environment may not perform equally well elsewhere.

Consequently, geospatial AI should be treated as a component of a rigorous geoinformatics workflow rather than a replacement for domain knowledge.

Cloud Computing and Geoinformatics

Cloud computing has introduced another major transformation.

Historically, processing large satellite datasets required substantial local storage and computational infrastructure. Cloud-based platforms allow users to access large geospatial datasets and computational resources remotely.

Cloud geospatial workflows can support:

• Large-scale satellite image processing

• Distributed spatial analysis

• Global environmental monitoring

• Machine learning

• Real-time dashboards

• Collaborative mapping

• Automated data pipelines

The shift toward cloud-native geospatial architecture is especially important as data volumes continue to grow.

Instead of downloading entire datasets to local computers, analysts can increasingly process data close to where it is stored.

Web GIS and Location Intelligence

Web GIS extends geospatial capabilities through internet-based applications.

Users can interact with maps through browsers and mobile applications without installing specialized desktop GIS software.

Web GIS commonly supports:

• Interactive maps

• Dashboards

• Search tools

• Data visualization

• Real-time sensor feeds

• Routing

• Public information portals

• Collaborative editing

Location intelligence builds upon these capabilities by using geographic information to support business and organizational decisions.

Businesses may use location intelligence for:

• Market analysis

• Retail site selection

• Logistics optimization

• Supply-chain planning

• Customer segmentation

• Risk assessment

• Asset management

This demonstrates that geoinformatics is not restricted to government agencies or scientific institutions. It is increasingly embedded in commercial decision-making.

Major Applications of Geoinformatics

Urban Planning and Smart Cities

Urban areas are complex spatial systems containing buildings, transportation networks, utilities, populations, economic activities, and environmental processes.

Geoinformatics enables planners to integrate these datasets within a common spatial framework.

Applications include:

• Land-use planning

• Urban growth analysis

• Transportation planning

• Infrastructure management

• Utility mapping

• Green-space assessment

• Population analysis

• 3D city modeling

• Smart-city dashboards

Three-dimensional GIS and digital twins are increasingly being used to represent buildings and infrastructure in urban environments.

Environmental Management

Environmental management is one of the most important applications of geoinformatics.

Remote sensing and GIS can be used to monitor:

• Deforestation

• Desertification

• Wetland change

• Coastal erosion

• Water quality

• Habitat fragmentation

• Soil degradation

• Vegetation health

Time-series satellite imagery allows analysts to examine environmental changes over months, years, or decades.

Geoinformatics can therefore provide a spatial evidence base for environmental monitoring and resource management.

Agriculture and Precision Farming

Agriculture is becoming increasingly data-driven.

Precision agriculture uses spatial information to manage agricultural resources according to variations within fields.

Geoinformatics can support:

• Crop mapping

• Soil variability analysis

• Irrigation planning

• Yield estimation

• Crop-health monitoring

• Variable-rate application

• Pest and disease detection

Satellite imagery, UAV imagery, GNSS, soil sensors, and GIS can be integrated into a single agricultural decision-support system.

This can help farmers and agricultural managers understand spatial variability and improve resource allocation.

Disaster Management and Emergency Response

Natural and technological disasters have strong spatial dimensions.

Geoinformatics can support every stage of the disaster-management cycle:

1. Risk assessment

2. Preparedness

3. Early warning

4. Response

5. Damage assessment

6. Recovery

7. Reconstruction

During floods, for example, satellite imagery and elevation models can be used to estimate inundation.

During wildfires, remote sensing can support burned-area mapping and fire monitoring.

After earthquakes, high-resolution imagery can assist with damage assessment and infrastructure evaluation.

GIS-based emergency systems can combine hazard zones, population distributions, roads, hospitals, shelters, and communication infrastructure to support operational planning.

Transportation and Logistics

Transportation networks are naturally spatial.

GIS and geoinformatics can support:

• Route optimization

• Fleet tracking

• Traffic analysis

• Road-network management

• Infrastructure planning

• Public transportation analysis

• Navigation

• Logistics optimization

Network analysis allows organizations to evaluate routes based on distance, travel time, cost, capacity, restrictions, or other network attributes.

When combined with real-time positioning and traffic information, geospatial systems can support dynamic transportation management.

Natural Resource Management

Natural resources are distributed geographically, making geoinformatics highly relevant to their management.

Applications include:

• Mineral exploration

• Forestry

• Water-resource management

• Fisheries

• Soil mapping

• Petroleum exploration

• Renewable-energy planning

In mineral exploration, spatial datasets from geology, geochemistry, geophysics, remote sensing, and terrain analysis can be integrated to identify areas with particular characteristics.

In forestry, satellite imagery and LiDAR can help estimate forest structure and monitor change.

Public Health and Epidemiology

Location is an important dimension of public health.

Geoinformatics can support spatial epidemiology by analyzing relationships between health events and geographic factors.

Applications include:

• Disease mapping

• Healthcare accessibility

• Population analysis

• Environmental exposure assessment

• Health-service planning

• Epidemiological surveillance

Geospatial analysis can reveal spatial patterns that may be difficult to detect using non-spatial datasets alone.

However, health-related geospatial applications require particularly careful treatment of privacy, confidentiality, data governance, and spatial resolution.

Climate Change and Environmental Monitoring

Climate change involves processes occurring across large spatial and temporal scales.

Geoinformatics provides essential tools for monitoring:

• Glacier retreat

• Sea-level-related changes

• Vegetation dynamics

• Land-surface temperature

• Drought

• Coastal change

• Snow cover

• Extreme events

Long-term Earth-observation archives enable researchers to compare environmental conditions across decades.

Combining satellite observations with climate models, ground measurements, and spatial statistics can provide a more comprehensive understanding of environmental change.

Geoinformatics in Surveying and Construction

Construction projects increasingly depend on accurate spatial information.

Modern workflows can combine:

• GNSS

• Total stations

• UAV photogrammetry

• LiDAR

• BIM

• GIS

• 3D visualization

This integration supports topographic surveys, earthwork calculations, progress monitoring, asset documentation, and quality control.

The convergence of Building Information Modeling and geospatial technologies is particularly significant.

While BIM traditionally emphasizes detailed information about buildings and infrastructure, GIS provides broader geographic context.

Their integration creates opportunities for more comprehensive infrastructure information management.

Digital Twins and Geospatial Technology

A digital twin is a digital representation of a physical object, system, or environment that can be connected to real-world data.

Geospatial technology is essential for digital twins because physical systems exist in geographic space.

A city digital twin might contain:

• Buildings

• Roads

• Bridges

• Utilities

• Terrain

• Sensors

• Traffic information

• Environmental observations

Digital twins can support simulation, visualization, monitoring, and scenario analysis.

The concept is increasingly relevant to smart cities, infrastructure management, industrial facilities, and environmental systems.

The Role of Open Geospatial Data

Open geospatial data has accelerated innovation.

Open datasets can provide information about:

• Roads

• Administrative boundaries

• Elevation

• Land cover

• Hydrography

• Satellite imagery

• Population

• Environmental conditions

Open-source software has also lowered barriers to entry for geospatial professionals and researchers.

The broader open geospatial ecosystem encourages collaboration, reproducibility, education, and innovation.

Nevertheless, users should assess data quality, licensing conditions, update frequency, positional accuracy, completeness, and metadata before using open datasets for professional applications.

Data Quality in Geoinformatics

Geospatial data quality is multidimensional.

Important characteristics include:

Positional Accuracy

How closely does the recorded location correspond to the true location?

Attribute Accuracy

How accurately do descriptive attributes represent the real-world feature?

Completeness

Are all required features represented?

Temporal Accuracy

Does the dataset accurately represent the relevant time period?

Logical Consistency

Does the dataset obey its intended structural and topological rules?

Resolution

What level of geographic detail does the dataset support?

A highly detailed dataset is not automatically a highly accurate dataset.

Professionals must evaluate whether data quality is appropriate for the intended application.

Challenges Facing Geoinformatics

Despite rapid technological development, geoinformatics faces several important challenges.

Data Volume

Modern sensors generate enormous quantities of information.

High-resolution imagery and LiDAR point clouds can require substantial storage and processing resources.

Data Integration

Different datasets may have different:

• Coordinate systems

• Resolutions

• Formats

• Acquisition dates

• Accuracy levels

• Attribute structures

Integrating them requires careful preprocessing.

Interoperability

Geospatial systems need to exchange information effectively.

Standards and interoperable services are therefore important for building connected geospatial infrastructures.

Privacy

Location data can be sensitive.

Mobile positioning, household information, health records, and individual movement patterns may create privacy risks.

Skills Gap

Modern geoinformatics requires expertise across multiple domains, including:

• GIS

• Remote sensing

• Programming

• Databases

• Statistics

• Cartography

• GNSS

• Machine learning

• Cloud computing

Professionals increasingly need multidisciplinary skills.

Model Uncertainty

Spatial models are simplifications of reality.

Predictions should therefore be accompanied by appropriate uncertainty assessment, validation, and documentation.

Essential Skills for a Geoinformatics Professional

A successful geoinformatics specialist often develops skills across several areas.

GIS

Professionals should understand:

• Spatial data structures

• Geoprocessing

• Cartography

• Spatial analysis

• Coordinate systems

• Geodatabase design

Remote Sensing

Important skills include:

• Image preprocessing

• Spectral analysis

• Classification

• Change detection

• Image interpretation

• Accuracy assessment

Programming

Programming is increasingly valuable.

Commonly used languages include:

• Python

• JavaScript

• SQL

• R

Python is particularly useful for geospatial automation, data processing, machine learning, and GIS scripting.

SQL is fundamental for managing and querying spatial databases.

Statistics

Spatial statistics helps professionals understand patterns, relationships, spatial dependence, and uncertainty.

Visualization

Effective geospatial communication requires more than technically correct analysis.

Maps, dashboards, three-dimensional models, and interactive visualizations must communicate information clearly to their intended audience.

Career Opportunities in Geoinformatics

The geospatial industry provides opportunities across government, academia, engineering, environmental management, technology, agriculture, transportation, and private consulting.

Potential professional roles include:

• GIS Analyst

• GIS Specialist

• Geospatial Data Scientist

• Remote Sensing Analyst

• Photogrammetrist

• Surveying Specialist

• Geodesist

• Cartographer

• Spatial Database Administrator

• Geospatial Developer

• Earth Observation Analyst

• LiDAR Specialist

• UAV Mapping Specialist

• Geospatial AI Engineer

• GIS Project Manager

• Location Intelligence Analyst

The exact responsibilities vary by organization and specialization.

The Future of Geoinformatics

The future of geoinformatics will likely be defined by the convergence of several technologies.

Artificial Intelligence

AI will increasingly automate feature extraction, classification, prediction, and anomaly detection.

Real-Time Geospatial Systems

The Internet of Things, connected vehicles, sensors, and satellite systems will produce increasingly dynamic spatial datasets.

Cloud-Native Geospatial Infrastructure

Large datasets and distributed computing will increasingly be integrated into cloud environments.

Three-Dimensional Mapping

3D point clouds, meshes, digital twins, and city models will expand beyond specialized applications.

Autonomous Mapping

UAVs, mobile mapping systems, and autonomous platforms will increasingly acquire geospatial data with reduced human intervention.

Spatial Computing

Augmented reality and virtual reality can provide new ways of interacting with geographic information.

Geospatial Foundation Models

Large AI models trained on combinations of imagery, geographic information, language, and other datasets may create new approaches to geospatial analysis.

These developments will increase the importance of data governance, model validation, interoperability, cybersecurity, and professional standards.

Geoinformatics and Sustainable Development

Geoinformatics has a direct relationship with sustainable development because many sustainability challenges have geographic dimensions.

Consider issues such as:

• Water scarcity

• Food security

• Urban expansion

• Climate adaptation

• Biodiversity loss

• Deforestation

• Disaster risk

• Infrastructure inequality

Each involves spatial patterns and relationships.

Geoinformatics allows organizations to identify where problems occur, how they change over time, which populations or ecosystems are affected, and where interventions may be required.

Consequently, geospatial information is increasingly treated as strategic infrastructure rather than merely a mapping resource.

Why Geoinformatics Matters

The central value of geoinformatics lies in its ability to connect location with information.

A conventional dataset may tell an organization how many assets it owns.

A geospatial database can additionally show:

• Where those assets are located

• What surrounds them

• How they are connected

• Which risks affect them

• How accessibility varies

• How conditions have changed over time

This spatial dimension can fundamentally change the way information is interpreted.

Geoinformatics therefore provides a framework for moving from isolated observations toward integrated spatial intelligence.

Conclusion

Geoinformatics has evolved from a collection of specialized mapping and measurement technologies into a comprehensive discipline for understanding the Earth through spatial information.

Its foundations include GIS, remote sensing, GNSS, surveying, photogrammetry, LiDAR, geodesy, spatial databases, and cartography. Modern developments in artificial intelligence, cloud computing, UAVs, three-dimensional modeling, digital twins, and real-time sensor networks are expanding the capabilities of the field even further.

The importance of geoinformatics is ultimately driven by a simple principle: almost every physical, environmental, social, and economic process occurs somewhere.

Understanding that "where" provides context that conventional information systems often cannot provide.

From urban planning and precision agriculture to disaster management, environmental monitoring, infrastructure development, transportation, public health, and climate science, geoinformatics enables organizations to collect, integrate, analyze, and communicate spatial information.

The future will not be defined solely by more accurate maps or larger datasets. It will be defined by the ability to transform massive and diverse geospatial observations into reliable, understandable, and responsible spatial intelligence.

As geographic information becomes increasingly connected to artificial intelligence, cloud platforms, autonomous sensors, digital twins, and real-time systems, geoinformatics will remain a foundational technology for understanding a rapidly changing world.

Keywords

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Suggested Primary Keyword

Geoinformatics

Suggested Secondary Keywords

Geospatial technology, GIS, remote sensing, GNSS, geospatial analysis, spatial data, GeoAI, LiDAR, photogrammetry, location intelligence