Spaceborne Remote Sensing, the observation of the features on ground from orbiting satellites is one of the expanding business areas of international space business, attracting the analysis of the markets and the growth patterns in the international arena. Whatever be the product/service, it is important for the market players to know the market characteristics to draw up marketing strategy and defining marketing mix, which becomes important for Remote Sensing services as well without an exception. The market for Remote Sensing data and services is influenced by a multitude of factors, which may seem unrelated at the outset. These may be Socio-Economic and Natural Resources related variables in addition to the expected product/market related variables. Hence the research work to model the market dynamics is taken up with the objectives of: 1)To identify factors influencing the market potential for Remote Sensing Data services 2)To model the relationship between these factors and the market potential 3)To validate and verify the model developed The analysis was carried out with 82 valid responses from respondents spread across 17 countries where Remote Sensing market is prevailing. A broad spectrum of economic, political, social, natural, and geographic variables believed to be influencing the Remote Sensing requirements were enumerated and validated through pilot study. Raw values for the variables were obtained from secondary sources and relative importance of these variables was determined through primary survey. The variables were synthesised into factors and captured into a regression model to arrive at the market potential index. This is statistically validated and also verified with market potential of a known country market, India. The analysis and results revealed that, market dynamics influencing market potential for Spaceborne Remote Sensing Data can be synthesised as the following three factors, explaining 80% of the variance in the market potential. (i)Politico Developmental factor-variables border related (Number of border countries, Border length, level of Border Disputes, Energy Production & Consumption, Economy and Information & Communication Infrastructure) (ii)Geo-Natural factor: represented by extent of Natural Resources, Energy Reserves etc. (iii)Market/Product related factor variables like product features, level of competition, availability of substitutes, current market position, and growth rate etc. The emergence of variables representing the developmental aspects on one side and political aspects on the other, as important factor is the outcome formally brought to recognition by the study. This is manifesting the growing importance of developmental activities and theatres of war as important additional market drivers in high and very-high resolution data services across the globe, in addition to the traditional applications like natural resources monitoring and management.

No description available.

The research uses cameras mounted to a vehicle to capture geotagged images while conducting a transect survey. The images from two capture dates were manually classified into different classes of previous crop, tillage systems, residue cover, and cover crop utilization. The raw data was compared against the Indiana Cropland Transect Survey and the USDA-NASS Cropland Data Layer. The symmetric Kullback-Liebler divergence method was used to compared the distributions looking for similarities. <div><br></div><div>The manually classified data was then used to build satellite segmentation models using artificial neural networks , decision trees, k nearest neighbors, random forests, and support vector machine methods. The models were compared using overall accuracy, kappa coefficient, specificity, sensitivity, positive prediction value, and negative prediction value. The best model for each category of previous crop, tillage system, residue cover, and cover crop was used to segment a Sentenial-2 imagery downloaded from Copernicus Open Access hub. The results of the segment were compared by looking at the agreement at individual pixel locations from the segmented raster to the manually classified data and the Indiana Cropland Transect Survey. </div><div><br></div><div>Finally, all the images captured were used to being the development of a automated image classifier using nested convolutional neural networks (CNN). A small set of images was used to build the CNN. That model when then make prediction on new unclassified images. The predictions were manually checked. The check images were used to the to build the training and validation pools for the models. The first network divided the images into field or not field.</div><div>The second branch was field images divided in to images containing green growing plants of brown dead plants or residues. The final branch was determining the amount of surface cover left on a field. The results from each run of the training process were saved and used to assess model performance looking at accuracy and loss.</div>

<p>Emerging mobile mapping systems include a wide range of platforms, for instance, manned aircraft, unmanned aerial vehicles (UAV), terrestrial systems like trucks, tractors, robots, and backpacks, that can carry multiple sensors including LiDAR scanners, cameras, and georeferencing units. Such systems can maneuver in the field to quickly collect high-resolution data, capturing detailed information over an area of interest. With the increased volume and distinct characteristics of the data collected, practical quality control procedures that assess the agreement within/among datasets acquired by various sensors/systems at different times are crucial for accurate, robust interpretation. Moreover, the ability to derive semantic information from acquired data is the key to leveraging the complementary information captured by mobile mapping systems for diverse applications. This dissertation addresses these challenges for different systems (airborne and terrestrial), environments (urban and rural), and applications (agriculture, archaeology, hydraulics/hydrology, and transportation).</p> <p>In this dissertation, quality control procedures that utilize features automatically identified and extracted from acquired data are developed to evaluate the relative accuracy between multiple datasets. The proposed procedures do not rely on manually deployed ground control points or targets and can handle challenging environments such as coastal areas or agricultural fields. Moreover, considering the varying characteristics of acquired data, this dissertation improves several data processing/analysis techniques essential for meeting the needs of various applications. An existing ground filtering algorithm is modified to deal with variation in point density; digital surface model (DSM) smoothing and seamline control techniques are proposed for improving the orthophoto quality in agricultural fields. Finally, this dissertation derives semantic information for diverse applications, including 1) shoreline retreat quantification, 2) automated row/alley detection for plant phenotyping, 3) enhancement of orthophoto quality for tassel/panicle detection, and 4) point cloud semantic segmentation for mapping transportation corridors. The proposed approaches are tested using multiple datasets from UAV and wheel-based mobile mapping systems. Experimental results verify that the proposed approaches can effectively assess the data quality and provide reliable interpretation. This dissertation highlights the potential of modern mobile mapping systems to map challenging environments for a variety of applications.</p>

Indiana University-Purdue University Indianapolis (IUPUI) / The primary objective of this research involves mapping out and validating the existence of sub surface drainage tiles in a given cropland using Remote Sensing and GIS methodologies. The process is dependent on soil edge differentiation found in lighter versus darker IR reflectance values from tiled vs. untiled soils patches. Data is collected from various sources and a primary classifier is created using secondary field variables such as soil type, topography and land Use and land cover (LULC). The classifier mask reduces computational time and allows application of various filtering algorithms for detection of edges. The filtered image allows an efficient feature recognition platform allowing the tile drains to be better identified. User defined methods and natural vision based methodologies are also developed or adopted as novel techniques for edge detection. The generated results are validated with field data sets which were established using Ground Penetration Radar (GPR) studies. Overlay efficiency is calculated for each methodology along with omission and commission errors. This comparison yields adaptable and efficient edge detection techniques which can be used for similar areas allowing further development of the tile detection process.