Company
Portfolio Data
Back to Company SearchTCARTA MARINE LLC
UEI: P3R2T76HPYX3
Number of Employees: 10
HUBZone Owned: Yes
Woman Owned: Yes
Socially and Economically Disadvantaged: No
SBIR/STTR Involvement
Year of first award: 2018
3
Phase I Awards
2
Phase II Awards
66.67%
Conversion Rate
$501,513
Phase I Dollars
$1,149,126
Phase II Dollars
$1,650,639
Total Awarded
Awards
Non-Invasive Quantification of Littoral Blue Carbon Habitats with Space-Based Hyperspectral Imaging
Amount: $175,000 Topic: 9.3
This project proposal addresses the pressing need to understand and quantify the discernable human impact on global climate, specifically focusing on the assessment of seagrass in coastal and intertidal ecosystems. Using innovative methods applied to high-resolution hyperspectral satellite imagery, TCarta aims to advance research on “The Changing Ocean” by providing detailed insights on the spatial and spectral dynamics of seagrass habitats. Traditional satellite sensors have lacked both the spatial and spectral resolution necessary for precise mapping of underwater changes at a local ecosystem level. Through a multi-sensor approach, leveraging Pixxel’s hyperspectral imagery, TCarta offers a breakthrough in benthic habitat classification and health assessment. This project outlines five key technical objectives, including the identification of optimal sensor bands, determination of water column properties, and the development of tools for efficient data processing and noise reduction. By developing automated analysis of hyperspectral imagery, TCarta aims to create accurate maps addressing critical ocean ecosystems such as seagrass beds, coral reefs and mangrove forests. The project’s significance lies in its potential to revolutionize marine habitat management, restoration, monitoring and risk assessment.
Tagged as:
SBIR
Phase I
2024
DOC
NOAA
Optimization of high resolution multispectral satellite imagery collection for Arctic conditions through custom sensor parameterization and multi-sensor integration for through-the-water space-based seafloor imaging in support of coastal hydrographic surv
Amount: $399,126 Topic: 9.6.03
A long time ago before GPS, satellites and multibeam sonars one Old Man and the Sea thought, “It is better to be lucky. But I would rather be exact. Then when luck comes you are ready.” There is no room for luck when conducting hydrographic surveying in Arctic coastal waters. Satellites can play a key role in operational efficiency, cost effective use of existing technology and risk reduction. More precise and extensible satellite-based technologies, when leveraged to inform hydrographic surveying operations, allow operators to be more exact in their operations. To fully exploit the precision of Earth observation multispectral satellites for marine applications and specifically for Arctic conditions of short collection timeframes, challenging weather conditions and highly dynamic coastal areas, imagery collection must be exact and optimized to the nuances and unknowns inherent to the region. This research will integrate space-based multispectral, laser and radar sensors in a multi-sensor approach, using one sensor to inform the collection scheme for another, to be exacting and precise when deploying these assets. This research fuses multiple existing space-based technologies and new applications for future satellite constellations through cross-platform systems integration and near real-time interoperability with hydrographic survey operations and routines.
Tagged as:
SBIR
Phase II
2021
DOC
NOAA
An Innovative Approach to Seafloor Classification: Applying Advanced Unsupervised Machine Learning in Complex and Variable Bottom Types with Multispectral Satellite Imagery
Amount: $102,858 Topic: 9.6.03
Machine learning and unsupervised classification have been applied to seafloor classification and benthic mapping using multibeam echosounders (MBES), Light Ranging and Detection (LiDAR), airborne hyperspectral, underwater optical cameras, and satellite imagery. Regardless of the sensor, data dimensionality reduction is an integral processing step of seabed mapping workflows . The most commonly implemented pre-classification dimensionality reduction technique is Principal Components Analysis (PCA). TCarta proposes research into a powerful alternative to PCA, Unsupervised Topological Data Analysis (UTDA) to address the established methods’ shortcomings as applied to WorldView 2/3 multispectral imagery. This research will offer a direct comparison of these two dimensionality reduction tools for three scenarios. The first scenario, with training data input and in situ control, will be St. Croix, US Virgin Islands; the second, Puerto Rico, within the same geographical region with no further training applied to determine predictive ability and accuracy; and the final test area will be a remote location with no local training applied, Kiribati. This research will determine the feasibility of this alternative to established unsupervised classification methods for seafloor classification and spectra-based data dimensionality reduction. If successful this research could lead to a potentially highly scalable and predictive tool for multi-sensor marine geospatial analysis.
Tagged as:
SBIR
Phase I
2020
DOC
NOAA
SBIR Phase II: Trident Bathymetry Mapping System: A Three-Pronged Automated Solution to Satellite Derived Shallow Seafloor Surveying
Amount: $750,000 Topic: MI
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project will be to address the lack of advanced maps of shallow water zones, or littoral zones, crucial to global ecology and economy. An estimated 70% of the world's littoral zone is not mapped to modern standards, and traditional methods of mapping these areas are prohibitively expensive and dangerous. Through remote sensing techniques, this innovation will produce detailed bathymetric data for shallow water areas around the globe at far lower costs than traditional methods. This is particularly significant for impoverished remote, low-lying islands with scarce resources for mapping the natural environment on which their economies heavily rely. Additionally, shallow water bathymetry data is vital to geospatial intelligence for amphibious landings and accurate assessment of the environmental impact of projects relating to global trade/port development, aquaculture, tourism, and resource development. The resulting geospatial products will provide a modern baseline of seabed topography for future scientific analysis, change detection, and greater understanding of the marine environment. The proposed project addresses the challenges of nearshore bathymetry mapping by advancing three distinct methods of calculating satellite-derived bathymetry. These methods rely on different physical parameters of measurement for water depth retrieval to form complementary measurements. Extraction methods utilize underwater stereophotogrammetry from multiple overlapping images, wave kinematic detection from multispectral imagery, and measurement of variable attenuation of multispectral signals through the water column. Research will be conducted on computer vision applications for assessing and allocating images based upon metaocean and atmospheric parameters; seafloor spectral segmentation techniques and effects on subsequent multispectral depth retrieval; automation of stereophotogrammetric seafloor target identification and correlation; and integration and automation of wave kinematic derived depth values with multispectral depth retrieval values. The shallow seafloor is a dynamic and poorly understood domain due to the complexity of the environment and navigational dangers of accessing unknown waters. The project's goal is to create a remotely produced, automated, self-validating satellite-based shallow seafloor mapping system by implementing artificial intelligence, advanced remote sensing and computer science algorithms and ongoing collection of earth observation imagery to globally produce 10-30 m resolution bathymetry elevation models. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Tagged as:
SBIR
Phase II
2019
NSF
SBIR Phase I: Project Trident: A Three-Pronged Automated Solution to Satellite Derived Littoral Bathymetry Mapping
Amount: $223,655 Topic: MI
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will be the establishment of the analytical framework and software for a global bathymetry mapping system based upon freely available satellite imagery, three independent extraction methods, and ongoing data collection to create geospatial products for a wide range of applications.? Shallow water zones are crucial to the world?s ecology, economy, and culture. An estimated 70% of the world?s littoral zone is not mapped to modern standards, and traditional methods of mapping these areas are prohibitively expensive and dangerous. Through remote sensing techniques, this innovation will produce detailed bathymetric data for shallow water areas around the globe at far lower costs than traditional methods. This is particularly significant for impoverished remote, low-lying islands which have scarce resources for mapping the natural environment on which their economies heavily rely. ?Additionally, shallow water bathymetry data is vital to geospatial intelligence and to accurately assessing the feasibility and environmental impact of projects relating to global trade/port development, aquaculture, tourism, and resource development.? The results of this innovation will provide a modern baseline of seabed topography for future scientific analysis, change detection, and greater understanding of the marine environment. The proposed project addresses the budgetary and health and safety challenges of nearshore bathymetry mapping by advancing three distinct methods of calculating satellite derived bathymetry. These methods rely on different parameters of measurement for water depth retrieval, enabling the advantages of one method to overcome the shortcomings of another. This will create a self-validating method for leveraging advanced remote sensing algorithms in an integrated software platform with ongoing data collection to produce 10-30m resolution bathymetry elevation models.? Extraction methods utilize underwater stereophotogrammetry from multiple overlapping images, wave kinematic detection from multispectral imagery, and measurement of variable attenuation of multispectral signals through the water column.? Research will be conducted on computer vision applications for assessing and allocating images based upon metocean and atmospheric parameters; seafloor spectral segmentation techniques and effects on subsequent multispectral depth retrieval; automation of stereophotogrammetric seafloor target identification and correlation, and integration and automation of wave kinematic derived depth values with multispectral depth retrieval values.? Statistical analysis of resulting data from this combined processing method, evaluated against in situ data for trial locations, will provide direct comparisons for accuracy reporting, algorithm refinements, computational optimization, and determine overall technical and commercial feasibility of future global implementation.? This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Tagged as:
SBIR
Phase I
2018
NSF