EO-Insight

Delivering innovative earth observation insight solutions that help organizations in the agriculture, energy development, forestry, infrastructure, land planning, and urban development industries.

The image generated using a lidar point cloud shows a 3D rendering of the Penn State Arboretum and surrounding communities. The rendering is done in different colors to highlight various features of the area. The image depicts several buildings and structures, many of which are surrounded by lush greenery and trees. The trees are rendered in green and cover most of the area. The buildings are rendered in various shades of gray, while the ground is rendered in brown. There are several areas of open space, some of which contain roads or other features. The area appears to be surrounded by a large hill or mountain.

This image depicts an existing single-family lot development featuring a two-story, gabled house, a shed, trees, and garden plots, with a street in the front and an alley in the rear of the lot. Above the picture of the lot are four layers that depict differing variables related to the urban heat island: land cover, land surface temperature, impervious and pervious surface cover, and phenology. The land cover layer has pavement in dark gray, buildings in black, lawn in green, and garden area in brown. The surface temperature is represented by pixels ranging from dark blue to red, indicating a cool to hot temperature gradient. The impervious and pervious surfaces are reflected by green for pervious and dark grey for impervious. The phenology is represented by a gradient from light to dark green.

Services

Geospatial Analysis, Modeling, and Simulation

Unsure if policies and regulations are being followed?

Specializing in analyzing satellite, airborne, and ground-based remote sensing data to monitor critical changes in urban infrastructure, land use, and resources.

Visualization and Communication

Having trouble visualizing and communicating complex spatial issues?

Creating interactive maps, dashboards, and data visualizations that simplify complex geospatial data.

Spatial IT Infrastructure

Worried about data formats, cybersecurity, and IT compliance?

Collaborating with GIS and IT departments to align infrastructure with operational needs, cybersecurity standards, and compliance goals.

The image presents a satellite view of a neighborhood, with buildings depicted in black and the greenness of vegetation (NDVI) represented using a green gradient. The NDVI is a measure of the health and density of vegetation, with higher values indicating more vegetation. Buildings: Depicted in black Scattered throughout the neighborhood Varying sizes and shapes Some appear to be single-family homes, while others may be apartments or commercial buildings Vegetation: Represented using a green gradient Ranges from light green to dark green More vegetation is present in the areas with higher NDVI values Vegetation is more dense in the areas with higher NDVI values Roads: Depicted in white Crisscrossing the neighborhood Varying widths and shapes Some roads appear to be major highways, while others are smaller streets Overall, the image provides a clear view of the neighborhood's layout and vegetation patterns. The use of a green gradient to represent NDVI values allows for a quick and easy visualization of the health and density of vegetation in different areas.

The image depicts a mean radiant temperature simulation performed using ENVI-Met, a software tool for urban and environmental planning. The simulation is represented by a 2D map, with different colors and symbols used to represent various features and conditions. Key Features: Buildings: Black Trees: Green Wind Movement: Arrows Mean Radiant Temperature: Gradient from yellow, orange, red, and magenta Simulation Results: The simulation results show a clear pattern of mean radiant temperature across the urban area. The temperature gradient is represented by a range of colors, with yellow and orange indicating lower temperatures and red and magenta indicating higher temperatures. The simulation also shows the impact of wind movement on the mean radiant temperature, with arrows indicating the direction and speed of the wind. Conclusion: The mean radiant temperature simulation performed using ENVI-Met provides valuable insights into the urban environment and the impact of various factors on the temperature. The simulation can be used to optimize urban planning and design to reduce the urban heat island effect and improve the comfort and well-being of residents. Key Features: Buildings: Black Trees: Green Wind Movement: Arrows Mean Radiant Temperature: Gradient from yellow, orange, red, and magenta Simulation Results: The simulation results show a clear pattern of mean radiant temperature across the urban area. The temperature gradient is represented by a range of colors, with yellow and orange indicating lower temperatures and red and magenta indicating higher temperatures. The simulation also shows the impact of wind movement on the mean radiant temperature, with arrows indicating the direction and speed of the wind. Conclusion: The mean radiant temperature simulation performed using ENVI-Met provides valuable insights into the urban environment and the impact of various factors on the temperature. The simulation can be used to optimize urban planning and design to reduce the urban heat island effect and improve the comfort and well-being of residents.

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about

Why EO-Insight?

EO-Insight does more than sell imagery or offer GIS consulting. We integrate cutting-edge remote sensing, advanced geospatial analytics, and secure IT infrastructure to deliver end-to-end solutions that empower organizations to act precisely and confidently. Our work does not stop at providing data—we ensure it is operational, interpretable, and impactful. That integrated approach sets us apart and positions our clients to lead.

Every day, we demonstrate our commitment to helping organizations move from static maps and siloed reports to dynamic, operational geospatial intelligence. This proven strategy helps our clients create stronger planning processes and decision-making, increase resilience, and deliver better results on every project.

Extensive industry experience

The team collectively has 60+ years of experience in earth observation and data analytics.

Insight innovations

Recognized leaders in converting imagery into decision-ready outputs, transforming raw pixels into actionable analysis.

Fluent across IT ecosystems

Building flexible, cost-effective solutions that fit your architecture, proprietary or open source.

This image presents a comparison of the simulated potential air temperature produced by ENVI-met 4.4.6 and ENVI-met 5 in a scenario with and without trees. The top-left section displays a land cover map with trees in dark green, buildings in black, grass in light green, and impervious surfaces in light grey. The bottom-left section shows a scenario without trees, while the top-right and bottom-right sections illustrate scenarios with trees. The highlighted areas in the top-right and bottom-right sections indicate that ENVI-met 4.4.6 miscalculates air temperature under trees. The temperature ranges from 34.5 °C (dark magenta) to 37.0 °C (yellow). The image suggests that ENVI-met 5 produces more accurate results than ENVI-met 4.4.6 in scenarios with trees. This is evident from the highlighted areas in the top-right and bottom-right sections, which show a more accurate representation of air temperature under trees. The image provides a clear visual comparison of the two models' performance in different scenarios. Overall, the image suggests that ENVI-met 5 is a more accurate and reliable model for simulating potential air temperature in scenarios with trees.

Comparing simulation software versions for scientific accuracy.

This image presents a map of New York City, illustrating the land surface temperature for June 2021. The map is color-coded, with red indicating the highest temperatures and blue indicating the lowest temperatures. The temperature ranges from 18.0 °C (blue) to 55.0 °C (red). The majority of the city's land surface temperature falls within the 25.0 °C to 30.0 °C range. The map provides a detailed view of the temperature distribution across the city, with the highest temperatures concentrated in the western and southern regions. The temperature distribution is likely influenced by various factors, such as the city's urban heat island effect, which can cause temperatures to be higher in urban areas compared to surrounding rural areas. The map also suggests that the city's temperature distribution is not uniform, with some areas experiencing higher temperatures than others. Overall, the map provides a valuable visual representation of the city's temperature distribution, which can be helpful in urban planning, public health, and other purposes.

This diagram shows a method for remote sensing roof type color, a stand-in for cool and dark roofs. The left three roofs are white and light grey, representing cool roofs. In contrast, the medium grey to dark black roofs on the right are dark roofs. The data used to create the new dataset are: 4-band high-resolution orthoimages, Landsat, LIDAR, building footprints, NYC borough boundaries, and zoning.

This image presents a map of New York City, illustrating the percentage of cool and dark roofs per borough and zoning district. The map uses a color-coded system, with lighter colors representing lower percentages of dark roofs and darker reds indicating higher percentages. The map shows that the majority of the city's roofs are dark, with only a few areas having a high percentage of cool roofs. The bar charts provide a more detailed breakdown of the percentage of cool and dark roofs per zoning type, organized by borough. The charts show that the percentage of dark roofs varies significantly across different zoning types and boroughs. For example, the charts show that the percentage of dark roofs is highest in the commercial and industrial zoning types, while the percentage of cool roofs is highest in the residential and commercial zoning types. Overall, the map and charts provide a valuable visual representation of the city's roof color distribution, which can be used to inform urban planning and policy decisions.