The mission of the Center for Geospatial Sciences, Applications and Technology (GEOSAT) is to advance geospatial knowledge and foster innovative geospatial technology and information solutions by promoting multidisciplinary collaborations between Texas A&M faculty and students, government, and industry.



  • Facilitate collaborative, multidisciplinary research between government, industry and academia
  • Offer hands-on education and training to Texas A&M and greater geospatial community
  • Form a network of multidisciplinary geospatial researchers
  • Provide geospatial resources and tools to affiliate researchers
  • Promote geospatial outreach activities



  1. Seminar Recap: Identifying valleys, peaks and rivers, PhD student Brennan Young improves geomorphometric characterizations of complex landforms and surface processes

    The average individual might not consider the amount of time, funds, and effort that went into creating the geological maps hanging on their wall. A geologist went out into the field; treaded through, potentially, tough and treacherous terrain; took numerous measurements; and struggled to knit the collected data together into a 2-D representation of the region. Ph.D. student Brennan Young points out this approach towards mapping is not very efficient.

    Brennan remarks, “It’s laborious and costly and invokes a significant degree of subjectivity.”
    Brennan Young is a Texas A&M geography Ph.D. student advised by Dr. Michael Bishop. In contrast to the more traditional, qualitative approach, together they are improving ways of quantitatively characterizing landforms, such as valleys, plateaus, mountain peaks, by applying semantic modeling and graph theory. In today’s technologically advanced and big data age, new means of mapping landforms and surface processes are emerging. Earth scientists now have access to a variety of GIS-based data collected via remote sensing. To process these large stores of data, geographers have further developed geomorphometry, a quantitative or mathematical approach to characterize geographical features and surface processes. Brennan highlights that this mathematical characterization of geographical features leads to improved consistency, objectivity, and cost-effectiveness throughout the mapping process. However, these mathematical algorithms’ ability to properly capture various geographical features still lags and makes mistakes signaling that further research is needed to fill in the gap. Dr. Michael Bishop and Brennan Young hope to fill the void by developing an improved approach using semantic modeling and graph theory. Instead of evaluating the terrain as a neighborhood of grid cells, hypsometric zones or set of fuzzy entities, graph theory provides a framework to evaluate the context and hierarchical relations between terrain entities. Semantic models combine mathematically defined parameters to identify specific topographic forms, which can be connected to other topographic forms, forming a network. The properties of these networks, such as the number of nodes (topographic forms) and the connectivity between the nodes, allow the method to consider the overall geographic structure and context when making evaluations about the overall structure of the topography.

    A hierarchical network of the upper mountain structure: summits (yellow triangles) that connect to ridge bifurcations (orange dots). These types of networks highlight the coupling of glacial erosion and tectonic uplift. (Source: Brennan Young)

    In their specific case study, Dr. Bishop and Brennan applied their approach to the Karakoram of the Himalayas, a region known for its broad array of geomorphic features and extreme relief. Using topographic data collected by Shuttle Radar Topography Mission (SRTM v3), their approach was able to identify various landforms and surface processes, such as glacial and river-formed valleys and zones of rapid tectonic uplift.  

    Baltoro glacier in the Karakoram Himalaya (Photo Credit: Guilhem Vellut) Source: “Glacier”, https://www.flickr.com/photos/o_0/55896170/, under creative commons license CC BY-SA 2.0, https://creativecommons.org/licenses/by-sa/2.0/

    The method developed by Dr. Bishop and Brennan can serve as a valuable resource for industries such as mining, oil exploration, and recreation. By providing a more effective means of mapping, industries can focus their efforts and minimize the cost and time spent by sending a team of scientists out into the Himalayas or other remote locations. Now geologists can remain within the cozy confinements of an office creating those detailed and accurate topographic maps hanging on their office walls, or at least that is what they are looking forward to. To find out more about GEOSAT seminar series and events, visit the events page by clicking here.
    Contributing Author Rachel Holanda

  2. Seminar Recap: Jack Lu shows us that mapping crop development with multiple, simultaneously-running autonomous vehicles is no easy feat 

     Han-Hsun (Jack) Lu remarks that “It is hard work waiting in 100 degrees outside collecting data.”
    Yet, based on the problems he and his fellow lab mates solve within their research, it appears that weather is probably the least challenging one.   Jack Lu is a master’s student working in Dr. Valasek’s Vehicle’s System and Control Laboratory within aerospace engineering. The research that the lab pursues is not at all trivial. The group endeavors to create an open architecture system in which both aerial and ground unmanned vehicles autonomously work together to complete a common task. This open architecture system enables the project to have a vast array of real-world applications. However, in the case of their particular study, the vehicles’ objective is to collect data about crop development within an entire field. For such a straightforward task, numerous logistical components must be identified and overcome.    During the GEOSAT seminar, Jack kindly demonstrated the overall project development and a few logistical aspects that have been refined and identified.   One particular aspect discussed was the challenges posed by mounted sensors. A variety of sensors are mounted to the unmanned vehicles and flown over the fields during their tests. These sensors collect infrared, multi-spectral, and hyperspectral images. The images are then used to evaluate the plants' traits and development. Each sensor has different mounting and operational needs. For example, a camera may only be able to collect quality data under a certain speed; therefore, the vehicle must autonomously know at which speed it needs to operate. Furthermore, the lab often uses the same unmanned vehicles to collect various types of data sets during a single visit. Indicating that the vehicles and various mounts need to be versatile enough to adapt quickly to each sensor. Additional hardware issues, such as limited battery life, limited or inexistent wireless networks, the rate of data transfer, among others, are some components in which the lab overcomes.   However, Jack outlines that one of the trickiest aspects is the software and programming components. Capturing all of the components of the system’s needs can be a real challenge and doing so in real-time is even harder. The lab has to consider the vehicles’ needs, the sensors needs, the communication needs between the vehicles and the control center, the data’s needs, and much more; and all of these components have to be embedded into the program so that eventually the vehicles can reach full autonomy.  

    Jack Lu demonstrates the mosaic of data collected and the area in which data needed improvement (Photo Credit: Rachel Holanda)

    One example of a software need is real-time analysis of the incoming data. When in the field, the vehicles go out and collect data; but when the researchers take the collected data and return to the lab to conduct a data analysis, they realize that either the data quality is low or incomplete. Perhaps the image the sensor took of that particular region was blurry or completely missing because a gust of wind pushed the vehicle during data collection. Jack outlining the problem explains that the researchers might not be able to go back and collect the missing data points until a few weeks later; and at that point, the crops’ characteristics would have already changed. Therefore, the lab needs an analysis to be conducted onsite and in real-time so the vehicle can adjust and compensate for this mistake during data collection. This places new pressures on the system’s hardware and software’s abilities. The researchers need to speed up data transfer rates from the vehicle to the control center conducting the analysis. They have to maximize the analysis’ running time so that conclusions about the data quality can be made quickly, and lastly, the program needs to issue the right commands so that the vehicles can return to the region in which the data point was missing.    Even with these various challenges, the research group continues advancing forward their system to complete autonomy. This is encouraging because the potential impact this project has for a variety of industries, not just agriculture, is very high. For example, this open architecture system of autonomously operating vehicles could be used to better manage crop development and disease control, assess the impact of a natural disaster such as a landslide or hurricanes, collect geographical information about remote locations such as deserts or mountain ranges, and more. For this reason, the lab is excited to push their system's capabilities, and the GEOSAT center is eager to see them achieve their goal!   To learn more about the research project, visit the lab’s website at https://vscl.tamu.edu/. If interested in attending a seminar, visit GEOSAT's event page 
    Contributing Author: Rachel Holanda

  3. Seminar Recap: PhD student Patricia Varela offers an integrated approach to estimating the regional risk of landslides

    PhD student Patricia Varela, of Civil Engineering’s Stochastic Geomechanics Laboratory, kicked off GEOSAT’s fall seminar series with an integrated approach to mapping the risk of landslides over a spatial domain. Patricia Varela and Dr. Medina-Cetina, civil engineering associate professor, used Bayesian Networks to integrate LiDAR derived products such as Canopy Height Models and Digital Terrain Models, with ancillary data into an integrated risk model. The variables used by this model consisted on an official landslide susceptibility map named SLIDO, a Factor of Safety evaluating the landslide stability, Vegetation Density and Wetness Index. This model was implemented to visually quantify different degrees of risk intensity for landslide susceptible regions within the Oregon Coast Range.

    "For decision-making purposes, it [Bayesian Networks] can be really relevant,” Patricia states, “it’s a very useful tool for integrating different types of information.”
    This study is not the first to apply Bayesian Networks to risk assessment of landslides susceptible regions. However, the study provides unique means of integrating Bayesian Networks into Geographical Information Systems (GIS), which can be an asset in today’s rapidly progressing GIS age. GIS has advanced to be more complex and accurate, leading to increased storages of data over a single geographical region. With this additional, higher quality data, more advanced methods are needed to interpret and analyze the region for improved understanding and decision-making.

    Ph.D. student Patricia Varela of the Stochastics Geomechanics Laborartory (SGL) presents the post-processing data analysis obtained from the remote sensing source maps (Photo Credit: Rachel Holanda)

    Another unique feature highlighted in the study is Bayesian Networks ability to calibrate and test its models. By using DOGAMI’s catalogued historic landslides within the region and by comparing it to the risk maps’ assessment of landslide, Patricia and Dr. Medina-Cetina were able to validate the Bayesian Network’s risk map estimates. This tool can be used to select the locations that requires the allocation of resources for minimizing the landslide vulnerability on the existent infrastructure of a given site, which represents a more efficient and improved decision making process. Civil Engineering masters student Miguel Ortiz Cahun found the calibration component very interesting, noting that the method used to calibrate the landslide risk maps may be applicable to other areas of study. He was interested in exploring if a similar calibration approach could be applied to his area of study. Overall, Patricia expressed satisfaction with the presentation’s reception by the audience. Additionally, Patricia remarks that the coupling of Bayesian Networks and GIS has many additional applications and looks forward to further exploring them. An application she has already implemented relates to assessing environmental risk among oil and gas infrastructure with drilling and production activities in Texas’ Barnett Shale. If interested in attending the remaining fall semester’s seminar presentations, see GEOSAT’s event page.
    Contributing Author: Rachel Holanda