Samapriya Roy, Ph.D.

Earth observation (EO) is undergoing a paradigm shift with the development of cloud‐based analytical platforms supporting EO data collection and access, parallel processing, easier communication of results, and expanded accessibility. As the global community of users and the diversity of applications grow, there is a clear need for expanded educational capacity to leverage these developments and increase the impact of EO research and teaching. Drawing upon extensive conversations between… Show more

Earth observation (EO) is undergoing a paradigm shift with the development of cloud‐based analytical platforms supporting EO data collection and access, parallel processing, easier communication of results, and expanded accessibility. As the global community of users and the diversity of applications grow, there is a clear need for expanded educational capacity to leverage these developments and increase the impact of EO research and teaching. Drawing upon extensive conversations between educators, practitioners, and researchers, we propose three pillars that must be prioritized to prepare students, researchers, and professionals to take full advantage of the cloud‐based EO paradigm and guide future growth. Show less

Reef halos are rings of bare sand that surround coral reef patches. Halo formation is likely to be the indirectly result of interactions between relatively healthy predator and herbivore populations. To reduce the risk of predation, herbivores preferentially graze close to the safety of the reef, potentially affecting the presence and size of the halo. Reef halos are readily visible in remotely sensed imagery, and monitoring their presence and changes in size may therefore offer clues as to how… Show more

Reef halos are rings of bare sand that surround coral reef patches. Halo formation is likely to be the indirectly result of interactions between relatively healthy predator and herbivore populations. To reduce the risk of predation, herbivores preferentially graze close to the safety of the reef, potentially affecting the presence and size of the halo. Reef halos are readily visible in remotely sensed imagery, and monitoring their presence and changes in size may therefore offer clues as to how predator and herbivore populations are faring. However, manually identifying and measuring halos is slow and limits the spatial and temporal scope of studies. There are currently no existing tools to automatically identify single reef halos and measure their size to speed up their identification and improve our ability to quantify their variability over space and time. Show less

Arctic beaded streams provide unique ecosystem functions and serve as important tundra habitats. Their unique ‘beads-on-a-string’ morphology is thought to form from thermokarst erosion, and they are densely represented in permafrost-ridden landscapes. Despite their ubiquity in high latitude regions, beaded stream formation and occurrence is not well studied, and beaded streams are not globally mapped. Access to these streams is challenging in their remote, dynamic environment, and up until… Show more

Arctic beaded streams provide unique ecosystem functions and serve as important tundra habitats. Their unique ‘beads-on-a-string’ morphology is thought to form from thermokarst erosion, and they are densely represented in permafrost-ridden landscapes. Despite their ubiquity in high latitude regions, beaded stream formation and occurrence is not well studied, and beaded streams are not globally mapped. Access to these streams is challenging in their remote, dynamic environment, and up until recently, monitoring these streams through satellite imagery was difficult given their relatively small size with channel widths of a few meters. The availability of high-resolution imagery from Planet data now makes it possible to detect and map these streams over large areas. Here we observe and predict the location of beaded stream catchments throughout the pan-Arctic domain Show less

High-resolution mapping and monitoring of global inland surface water bodies are critical to address challenges in sustainable water management practices. Planet currently operates the largest constellation of Earth Observation satellites and collects images at very high spatial (0.5 m - 5 m) and temporal (near-daily) resolutions. Here, we use PlanetScope data (resampled to 3 m) to develop a holistic and fully automated pipeline running on the Google Cloud Platform for monitoring global inland… Show more

High-resolution mapping and monitoring of global inland surface water bodies are critical to address challenges in sustainable water management practices. Planet currently operates the largest constellation of Earth Observation satellites and collects images at very high spatial (0.5 m - 5 m) and temporal (near-daily) resolutions. Here, we use PlanetScope data (resampled to 3 m) to develop a holistic and fully automated pipeline running on the Google Cloud Platform for monitoring global inland surface water. We incorporate the openly-available Global Reservoir and Dam (GRanD) data set into a three-stage supervised learning approach which initiates with an unsupervised label-generation step consisting of k-means clustering and NIR-based thresholding. We then rank the labels generated from these steps and the water labels extracted from the latest ESRI 10 m land cover data based on image contours. The best (noisy) labels having the least number of contours from this unsupervised learning stage are bootstrapped to train a deep-learning based semantic segmentation model (U-Net) on a KubeFlow pipeline. We subsequently create a new refined dataset by using these model predictions as labels which are passed to a Stochastic Gradient Descent (SGD)-based multi-temporal supervised label refinement stage (SGD classifier running on the same label for multiple input scenes). Finally, we iterate over the SGD based-supervised and U-Net-based label refinement steps to successively denoise the bootstrapped data until we obtain an acceptable test accuracy (F1 score > 0.9). Show less

Basemap and Planet Fusion—derived from PlanetScope imagery—represent the next generation of analysis ready datasets that minimize the effects of the presence of clouds. These datasets have high spatial (3 m) and temporal (daily) resolution, which provides an unprecedented opportunity to improve the monitoring of on-farm reservoirs (OFRs)—small water bodies that store freshwater and play important role in surface hydrology and global irrigation activities. In this study, we assessed the… Show more

Basemap and Planet Fusion—derived from PlanetScope imagery—represent the next generation of analysis ready datasets that minimize the effects of the presence of clouds. These datasets have high spatial (3 m) and temporal (daily) resolution, which provides an unprecedented opportunity to improve the monitoring of on-farm reservoirs (OFRs)—small water bodies that store freshwater and play important role in surface hydrology and global irrigation activities. In this study, we assessed the usefulness of both datasets to monitor sub-weekly surface area changes of 340 OFRs in eastern Arkansas, USA, and we evaluated the datasets main differences when used to monitor OFRs. When comparing the OFRs surface area derived from Basemap and Planet Fusion to an independent validation dataset, both datasets had high agreement (r2 ≥ 0.87), and small uncertainties, with a mean absolute percent error (MAPE) between 7.05% and 10.08%. Pairwise surface area comparisons between the two datasets and the PlanetScope imagery showed that 61% of the OFRs had r2 ≥ 0.55, and 70% of the OFRs had MAPE <5%. In general, both datasets can be employed to monitor OFRs sub-weekly surface area changes, and Basemap had higher surface area variability and was more susceptible to the presence of cloud shadows and haze when compared to Planet Fusion, which had a smoother time series with less variability and fewer abrupt changes throughout the year. The uncertainties in surface area classification decreased as the OFRs increased in size. Show less

Despite the tremendous advances in data-driven platforms, technologies, and analytical tools designed to ease collaboration between researchers and data scientists, very little attention has been devoted to understanding or developing the culture of collaboration—ie, how interpersonal dynamics between research professionals drive collaboration and the institutional roles that sponsors, universities, and experts play in the coproduction of knowledge.“Collaboratory cultures” is a people-first… Show more

Despite the tremendous advances in data-driven platforms, technologies, and analytical tools designed to ease collaboration between researchers and data scientists, very little attention has been devoted to understanding or developing the culture of collaboration—ie, how interpersonal dynamics between research professionals drive collaboration and the institutional roles that sponsors, universities, and experts play in the coproduction of knowledge.“Collaboratory cultures” is a people-first structure in the research ecosystem and necessary to support the next wave of data-driven “transdisciplinary” research. Individuals who possess the skills to lead transdisciplinary projects and the savvy to negotiate collaboratory cultures will be the most effective at advancing their research agendas.
This article is the result of the 2019 Lemon Labs workshop [1], where the authors of this article shared their collective experiences on a wide range of data-driven science issues. This “visioning lab” event provided an open, inviting space for participants to share the challenges they face in their own collaborative projects (see S1 Text). Lessons learned were summarized and developed into the following interconnected (see Fig 1) Ten Simple Rules within a “collaboratory cultures” framework. Show less

The Amazon Delta and Estuary (ADE) is a region of continental and global ecological importance. Controversy, many of the basic infrastructure and services essential for quality of life and sustainable development of this delta are absent. Using a conceptual model to define socio-economic vulnerability in the urban ADE, a thorough assessment of indicators including sanitation services, housing conditions, household income, population, flood risk and unplanned settlements was conducted in 41… Show more

The Amazon Delta and Estuary (ADE) is a region of continental and global ecological importance. Controversy, many of the basic infrastructure and services essential for quality of life and sustainable development of this delta are absent. Using a conceptual model to define socio-economic vulnerability in the urban ADE, a thorough assessment of indicators including sanitation services, housing conditions, household income, population, flood risk and unplanned settlements was conducted in 41 cities at the census sector scale (n = 2938). A multi criterion index was applied to classify urban vulnerability from three dimensions: flood exposure, socio-economic sensitivity and infrastructure. This is the first study to examine urban vulnerability within and between urban areas of the ADE. Results indicated that most of the urban sectors of the ADE are exposed to potential risks due to a combination of flood hazards, poverty and basic structural deficiencies such as insufficient drinking water or inadequate waste water collection, with several sectors being afflicted by similar problems. The assessment of vulnerability indicates that 60–90 % of the urban population live in conditions of moderate to high degree of vulnerability. The ADE cities presented a pattern where vulnerability increases from city center to their newly developed urban areas. Inadequate planning coupled with rapid urbanization has contributed to the development of unplanned settlements in almost half of the urban sectors of the ADE. Combined, these factors contribute to widespread socio-economic vulnerability along the urban spaces of the ADE, increasing exposure to health risks and more frequent seasonal and stochastic events such as storm surges and high flooding levels. Show less

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… Show more

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. Show less

Environmental systems modeling has always been at the core of gaining insight into the world. Most environmental systems behave very differently from fairly predictable systems because of nonlinearity in approach and behavior. This paper discusses the problem that exists in effectively modeling a multicriterion and nonlinear parametric system. A cyclical hierarchical model was proposed that allows the user to model the effects of various environmental parameters, which not only propagate as… Show more

Environmental systems modeling has always been at the core of gaining insight into the world. Most environmental systems behave very differently from fairly predictable systems because of nonlinearity in approach and behavior. This paper discusses the problem that exists in effectively modeling a multicriterion and nonlinear parametric system. A cyclical hierarchical model was proposed that allows the user to model the effects of various environmental parameters, which not only propagate as forward processes, but also may have dependencies and carryover effects on other primary set of parameters. Show less

This thesis investigates one of the leading challenges that plagues our future, the urban growth scenario and tries to model it and understand it using a Social network Analysis Module. Urban growth process has been studied and modelled for a long time to generate sustainable growth. However tendencies of Sprawl and city compactness have been integrating themselves into the understanding and the study has evolved from mere growth dynamics to understanding variations in patterns of growth… Show more

This thesis investigates one of the leading challenges that plagues our future, the urban growth scenario and tries to model it and understand it using a Social network Analysis Module. Urban growth process has been studied and modelled for a long time to generate sustainable growth. However tendencies of Sprawl and city compactness have been integrating themselves into the understanding and the study has evolved from mere growth dynamics to understanding variations in patterns of growth. Social Networking has been in existence for quite some time and coupled with Decision theory and group forming dynamics allows us to analyse the effect of decision making and social network structure on any outcome that we are interested in understanding. The growth of the city has first been analysed in various scales and comparative terms such as Population growth, trend line, and variation in urban built up for the urban city of Nagpur, in Maharashtra, Central India. A original method is suggested using Social network analysis which tends to map dynamic actor/ user decision in terms of secondary functions that might act as decision variables. The study is thus extrapolated to generate parameters or nodes of interest instead of specific members in the social network, allowing it to be used to generate centrality or importance of one parameter over the other and to generate future scenario based on the same. The methodology is applied based on input values derived from users and applied to model the parameters with high value of betweenness centrality and also other criterion which are shed light on different aspect of the secondary mapping and the Social Network as a whole and nodes of interests were identified for criterion based modelling. Show less