.svg)
Search The Site
.svg)
Search The Site
EXPLORE OUR TECHNOLOGY
EXPLORE OUR TECHNOLOGY
EXPLORE OUR TECHNOLOGY
EXPLORE OUR TECHNOLOGY
Barriers
Several barriers could impede the effective use of satellite data for addressing challenges related to urban planning, climate adaptation, and resilience. These barriers include:
Timing
Limited Temporal Frequency
Some Copernicus satellites have revisit times that might not be frequent enough to capture rapid changes in urban environments or to provide timely updates during emergency situations like floods or heatwaves.
Timing
Delay in Data Processing
The time taken to process and make the data available can also be a constraint, particularly in scenarios requiring near-real-time information.
Spacing
Insufficient Detail
The spatial resolution of some Copernicus datasets might not be fine enough to detect small-scale features or detailed urban infrastructure. For instance, Sentinel-2 provides data at a resolution of 10 meters, which might not capture fine details in dense urban settings.
Spacing
Variability in Resolution
Different Copernicus satellites offer varying spatial resolutions, which might lead to inconsistencies when integrating data from multiple sources.
Frequency
Revisit Intervals
The spatial resolution of some Copernicus datasets might not be fine enough to detect small-scale features or detailed urban infrastructure. For instance, Sentinel-2 provides data at a resolution of 10 meters, which might not capture fine details in dense urban settings.
Frequency
Cloud Cover
Optical satellites like Sentinel-2 can be impeded by cloud cover, affecting the frequency of usable images and necessitating reliance on synthetic aperture radar (SAR) data from satellites like Sentinel-1, which might not provide the same level of detail for certain analyses.
Technology
Data Handling and Processing
The sheer volume of Copernicus data can be overwhelming, requiring significant computational resources and expertise in data processing, which might not be available to all users.
Technology
Interoperability
Integrating Copernicus data with other datasets (e.g., local sensor data, IoT data) can be technically challenging, requiring standardized formats and protocols.
Technology
User Expertise
Effective use of Copernicus data requires a certain level of technical expertise in remote sensing and geospatial analysis, which might not be available to all urban planners or climate resilience practitioners.
.svg)
01.
Latitudo 40 can integrate data from multiple Copernicus satellites (e.g., Sentinel-1, Sentinel-2, Sentinel-3) to leverage the strengths of each dataset. By combining optical and SAR data, they can overcome limitations related to cloud cover and achieve more comprehensive monitoring.
Incorporating data from other satellite missions, ground-based sensors, IoT devices, and historical datasets can enhance the temporal and spatial resolution and provide a more complete picture.
02.
Latitudo 40 can apply advanced machine learning and AI algorithms to improve the accuracy and speed of data processing. Techniques such as deep learning can be used for super-resolution, enhancing the detail in satellite images beyond their native resolution. We have an operative solution to transform Sentinel 2 imagery to 1m resolution.
The use of generative AI to create synthetic satellite imagery can help simulate and analyze potential urban changes and climate impacts, providing valuable insights for planning and resilience.
03.
Developing intuitive platforms and dashboards that simplify the access, visualization, and interpretation of satellite data can lower the technical barriers for users. Tools like Urbalytics can provide actionable insights in an easily understandable format.
Implementing automated data processing pipelines can reduce the time and expertise required to handle large volumes of satellite data, making it more accessible to a broader range of users.
04.
Offering training sessions, workshops, and online courses to build technical capacity among urban planners, policymakers, and other stakeholders. This helps users understand and utilize the full potential of Copernicus data.
Partnering with academic institutions, research organizations, and industry experts to foster knowledge sharing and collaboration.
05.
Offering customizable solutions that address the unique needs of different municipalities and organizations. This includes providing specific modules for urban heat island analysis, green space management, flood risk assessment, etc.
Implementing pilot projects to showcase the effectiveness of Latitudo 40’s solutions in real-world scenarios. This can help build trust and demonstrate the practical benefits of using advanced satellite data and AI.
Technology description
Latitudo 40's revolutionary Super-Resolution 1m NDVI Layer uses advanced algorithms on Sentinel-2 satellite images to provide highly detailed vegetation health data. This 1-meter resolution technology is crucial for precision agriculture, enabling early detection of crop issues, optimizing water usage, and enhancing overall agricultural management.
Access high-resolution Land Surface Temperature (LST) data at 10-meter accuracy in Celsius (°C) with our tool.
The Surface Urban Heat Island (SUHI) layer assesses the Urban Heat Island effect, highlighting urban areas that are warmer than their rural surroundings.
The Albedo layer measures surface reflectivity, indicating the percentage of sunlight that is reflected into space.
The Heatwave Risk map integrates temperature data (hazard), population demographics (exposure), and area morphology (vulnerability) to generate a risk index ranging from 0 to 100.
The Park Cool Islands (PCI) layer distinguishes urban parks based on their cooling effects, categorizing them into Major and Minor Cool Islands.
The Microclimatic Performance Index (MPI) evaluates the effectiveness of Urban Green Infrastructure (UGI) in combating the Urban Heat Island (UHI) effect.
The Carbon Storage layer quantifies CO2 absorption by vegetation, offering a detailed view of nature's impact on atmospheric carbon reduction.
The Tree Cover Density (TCD) layer accurately depicts the percentage of an area covered by tree canopy, ranging from 0 to 100%.
Technology highlights
Uses high-resolution satellite imagery and sensors to gather real-time environmental data.
Employs AI and machine learning algorithms to process vast amounts of data quickly and accurately.
Provides predictive analytics and actionable insights to assess climate risks and urban planning strategies.
Offers user-friendly dashboards that display comprehensive visual data, including maps and indices, to facilitate informed decision-making for urban planners and stakeholders.
Latitudo 40 is an innovative startup in the field of Earth Observation, born from the passion, expertise, and experience of a team united by a common goal: to improve the planet through satellite technologies. We define ourselves as a lean organization, basing our success on the principles of user-centered design.
.svg)
