Data Science | Open Access Articles | Digital Commons Network™

Nci Research Impact And Expertise With Bibliometric Data, Senay Purzer, Wei Zakharov, Carla B. Zoltowski

Supplementary Content for Stewards of Data: A Practical Handbook for Undergraduate Researchers in Engineering and Applied Sciences

This poster explores NCI research impact and expertise with bibliometric data. This book was published by the Purdue University Press. Copyright 2026. Permission: Courtesy of Colin Roberson, Lonnie Schwartz, Vineeth Narra, and Pete E. Pascuzzi.

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Statistical Analysis Of Log Transformation Effectiveness In Air Traffic Movement Forecasting During Covid-19 In South Africa, John Lehlaka Masekoameng

Journal of Aviation Technology and Engineering

This study evaluates the effectiveness of log transformation in enhancing multiple regression models used to forecast air traffic movements (ATMs) in South Africa during the COVID-19 pandemic. Using 60 monthly observations from October 2016 to September 2021, the analysis incorporates variables such as revenue, lockdown levels, COVID-19 metrics, exchange rates, gross domestic product, and population. Two models are compared: one using raw ATMs and another with log-transformed ATMs as the dependent variable.

While the untransformed model shows stronger explanatory power (R² = 0.904, adjusted R² = 0.891) compared to the log-transformed model (R² = 0.772, adjusted R² = 0.741), the …

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Enhancing Education Through Virtual Reality: A Comparative Study Of Vr And Traditional Learning Environments, Shrivardhan Atluri

The Journal of Purdue Undergraduate Research

No abstract provided.

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A Deep Learning Framework With Explainable Ai For Atmospheric Blocking Detection And Interpretation, Devansh Khandelwal

Discovery Undergraduate Interdisciplinary Research Internship

Atmospheric blocking is a large-scale quasi-stationary phenomenon in mid-latitude circulation, characterized by persistent high-pressure systems that disrupt the typical west-to-east flow of the jet stream. These systems can cause extreme weather events—such as heatwaves, cold spells, or droughts—that persist for days or even weeks. This study proposes a deep learning framework to predict and interpret the occurrence of atmospheric blocking by integrating geophysical precursors such as geopotential height (Z500), stream function (SF200), and potential vorticity. These features, which are dynamically linked to blocking onset and persistence, serve as inputs to a Convolutional Neural Network model trained on the CESM Large …

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Crop Yield Prediction At Multiple Spatial Scales With Statistical Machine Learning, Vaibhav Charan, Pratishtha Poudel

Discovery Undergraduate Interdisciplinary Research Internship

Understanding and accurately predicting crop yield is becoming increasingly important today in the face of global food security challenges, and thus, the availability of standardized data and scalable models is the need of the hour. To support this, researchers have developed CY-Bench (Crop Yield Benchmark), a comprehensive dataset that helps forecast maize and wheat yields on a global scale. This research project primarily involved working with the CY-Bench dataset aiming to improve crop yield prediction through machine learning. Initially, papers explaining the CY-Bench dataset and other papers for agriculture modeling were studied and analyzed in detail. The research then progressed …

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Discovering And Designing Novel Perovskite Photovoltaic Materials Via Machine Learning, Junyeong Ahn

Discovery Undergraduate Interdisciplinary Research Internship

Perovskite semiconductors are promising materials for high-efficiency photovoltaics due to their outstanding optoelectronic properties, emerging as a sustainable energy source through solar cell applications. Perovskites with the ABX₃ composition (A, B = metal or organic cations with varying oxidation states; X = chalcogen or halogen anions) have gained interest for their excellent phase stability and compositional tunability. However, combinatorial possibilities arising from the many choices of A, B, and X site species, and their respective mixing fractions, a large number of possible ABX₃ perovskites remain undiscovered. In this work, we used machine learning (ML) methods to design new stable and …

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Mapping Responsible Workflows For Geospatial Data Science: Developing The I-Guide Data Ethics Toolkit, Peter T. Darch, Kyra M. Abrams, Ivan Y M Kong

I-GUIDE Forum

AI workflows in geospatial data science offer significant societal benefits but raise ethical, transparency, and reproducibility challenges. Current ethical frameworks and tools are often hard to integrate into daily research practice. This paper introduces the I-GUIDE Data Ethics Toolkit (DET), a lightweight suite designed for users of the NSF-funded Institute for Geospatial Understanding through an Integrative Discovery Environment (I-GUIDE). Based on a longitudinal mixed-methods study, including surveys, interviews, and observations, we identified five design priorities: usability, anticipatory planning, distributed responsibility, comprehensive coverage, and policy compliance. We integrated existing AI and data research lifecycles into an eight-stage I-GUIDE Research Lifecycle, serving …

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Machine Learning-Based Variance Analysis Of Brightness Temperature In Simulated Satellite Footprints, Chhaya R. Kulkarni, Nikki Prive, Vandana P. Janeja

I-GUIDE Forum

This study investigates the variance in brightness temperature (BT) within simulated satellite footprints for Observing System Simulation Experiments (OSSE), focusing specifically on Channels 5 and 11 of the Advanced Microwave Sounding Unit (AMSU-A). High-resolution atmospheric simulations from the DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains (DYAMOND) dataset were utilized to generate brightness temperature data using the Python interface for the Community Radiative Transfer Model (PyCRTM). A computational design map incorporating Random Forest and Association Rule Mining was employed to identify and validate key atmospheric variables influencing BT variance. This ensemble approach facilitated a deeper understanding of atmospheric …

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Application Of Physics-Informed Neural Networks On Crop Yield Prediction At Multiple Scales, Aditya P. Prabhu, Pratishtha Poudel, James V. Krogmeier

Discovery Undergraduate Interdisciplinary Research Internship

Accurately predicting crop yields is a critical challenge in sustainable agriculture, food security, and farm management. Traditional process-based models rely on agronomic domain knowledge, crop physiology and statistical approaches, while purely data-driven approaches leverage machine learning or deep learning models using meteorological and spatial data. Unfortunately, these black-box models(Data-drive approaches) often lack interpretability and fail to incorporate well-established physical principles. This project explores a hybrid approach by implementing Physics Informed Neural Networks, mainly, physics-based recurrent neural networks (PI-RNNs) for time-series yield prediction. PINNs allow for the integration of scientific knowledge directly into the model by embedding physical laws as constraints …

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Where To Build Food Banks: A Machine Learning Approach, Gavin Ruan

The Journal of Purdue Undergraduate Research

Over 44 million Americans currently suffer from food insecurity, of whom 13 million are children. Food insecurity has been shown to cause a wide range of both physical and developmental issues. Across the United States, thousands of food banks and pantries serve as vital sources of food and other forms of aid for food-insecure families. By optimizing food bank locations, food banks and their resources would become more accessible to families who desperately require it. The aim of this paper is to build a machine learning framework that is able to optimize food bank locations and to consider factors such …

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Predictive Maintenance Analysis Of Turbofan Engine Sensor Data, Stanley A. Melkumian

The Journal of Purdue Undergraduate Research

Predictive maintenance in aviation and aerospace applications is among the most explored problems in machine learning (ML) and artificial intelligence (AI), and datasets such as NASA’s C-MAPPS turbofan engine degradation simulation data have proven invaluable, helping researchers explore numerous questions on engine performance, maintenance, and failure. The purpose of this study was to extend the current research on predicting the remaining useful life (RUL) of engines and their risk classification. Starting with simple yet under-investigated nonlinear survival and random forest models, the analysis implemented eXtreme Gradient Boosting (XGBoost) and long short-term memory (LSTM) from TensorFlow’s Keras library. For both regression …

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Tradeoffs Of Generalization, Kyra M. Abrams, Peter T. Darch

I-GUIDE Forum

Models used in geospatial data science are often built and optimized for a specific local context, such as a particular location at a point in time. However, upon publication, these models may be generalized beyond this context, reused in research simulating or predicting other times and places. Without sufficient information or documentation, bias embedded in these models can in turn result in bias in the reuser’s research outputs. Drawing on a long-term qualitative case study of aging dams researchers and developers of models used by these researchers, we find significant documentation gaps. We combine a literature-based genealogy with interviews with …

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Smart Airports: Artificial Intelligence–Enabled Internet Of Things Networks Using Blockchain Technology, Edwin Ongola

Journal of Aviation Technology and Engineering

This article provides a perspective on how an internet of heterogeneous self-service airport terminal systems can be used for data collection, which is stored on a private or consortium blockchain depending on the ownership or operations of an airport or both. Such a setup would help to increase efficiency, reduce costs, and improve traveler experience at airport terminals. Moreover, it would allow airports to gather data directly from passengers as opposed to waiting to receive the same data from airlines. Subsequently, this data, now on a blockchain system, becomes a data source for other applications such as machine learning. In …

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Geospatial Analysis Of Agricultural Potential In The United States, Diana Febrita

Graduate Industrial Research Symposium

Traditionally, the agriculture sector is responsible for providing food and crop products. However, the role of agriculture has expanded beyond its traditional function. It is the main sector that contributes to the provision of food, income, employment, environmental protection, and local economic development. Reflecting on the roles of agriculture, understanding the potential of agriculture in the United States is crucial to discovering the prospects and challenges. This study will briefly discuss the agricultural potential in the United States based on the five assets, including natural capital, financial capital, human capital, physical capital, and social capital. To identify the states with …

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Accuracy Of Nitrate Hysteresis And Flushing For Agricultural Watersheds In The Midwest, Noah Rudko, Sara K. W. Mcmillian, Jane Frankenberger, François Birgand

Graduate Industrial Research Symposium

Storm event-based metrics, such as hysteresis (HI) and flushing (FI), are used to differentiate nitrate pathways and sources, which is essential for watershed management. Estimations of these event-based metrics typically use high frequency (15-minute – hourly) measurements, but daily data are also used due to their greater availability. To date, there has been no study assessing how using lower frequency samples affect the accuracy of HI and FI, which could skew interpretation of potential nutrient pathways and sources. We used continuous measurements of nitrate collected at 9 watersheds throughout the Midwest spanning 448 storms. HI and FI were estimated from …

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Characterization Of Biological Particles Using An Integrated Hyperspectral Imaging And Machine Learning, Kaeul Lim, Arezoo Ardekani

Graduate Industrial Research Symposium

Hyperspectral imaging (HSI) is a promising modality in medicine with many potential applications. This study focuses on developing a label-free lipid nanoparticle characterization method using a convolutional neural network (CNN) analysis of HSI images. The HSI data, hypercube, consists of a series of images acquired at different wavelengths for the same field of view, providing continuous spectra information for each pixel. Three distinct liposome samples were collected for analysis. Advanced image preprocessing and classification methods for HSI data were developed to differentiate liposomes based on their material compositions. Our machine learning-based classification method was able to distinguish different liposome types …

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Sepsis Treatment: Reinforced Sequential Decision-Making For Saving Lives, Dipesh Tamboli, Jiayu Chen, Kiran Pranesh Jotheeswaran, Denny Yu, Vaneet Aggarwal

Graduate Industrial Research Symposium

Sepsis, a life-threatening condition triggered by the body's exaggerated response to infection, demands urgent intervention to prevent severe complications. Existing machine learning methods for managing sepsis struggle in offline scenarios, exhibiting suboptimal performance with survival rates below 50%. Our project introduces the "PosNegDM: Reinforcement Learning with Positive and Negative Demonstrations for Sequential Decision-Making" framework utilizing an innovative transformer-based model and a feedback reinforcer to replicate expert actions while considering individual patient characteristics. A mortality classifier with 96.7% accuracy guides treatment decisions towards positive outcomes. The PosNegDM framework significantly improves patient survival, saving 97.39% of patients and outperforming established machine learning …

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Online Class-Incremental Learning For Real-World Food Image Classification, Siddeshwar Raghavan, Jiangpeng He, Fengqing Zhu

Graduate Industrial Research Symposium

Food image classification is essential for monitoring health and tracking dietary in image-based dietary assessment methods. However, conventional systems often rely on static datasets with fixed classes and uniform distribution. In contrast, real-world food consumption patterns, shaped by cultural, economic, and personal influences, involve dynamic and evolving data. Thus, it requires the classification system to cope with continuously evolving data. Online Class Incremental Learning (OCIL) addresses the challenge of learning continuously from a single-pass data stream while adapting to the new knowledge and reducing catastrophic forgetting. Experience Replay (ER) based OCIL methods store a small portion of previous data and …

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Modelling The "Bottom-Up" Development Pattern Of Tar Spot Disease In Corn, Brenden Lane, Joaquín Guillermo Ramírez-Gil, Carlos Góngora-Canul, Mariela Sofia Fernandez Campos, Andres Cruz-Sancan, Fidel E. Jiménez-Beitia, Alex G. Acosta-Guatemal, Wily Sic, C. D. Cruz

Graduate Industrial Research Symposium

In 2015, the corn-infecting pathogen Phyllachora maydis (causal agent of tar spot disease) was reported for the first time in the United States. The disease has since spread across the US, causing major yield losses. In 2021 alone, 5.88 million metric tons (231.3 million bushels) of US corn yield were lost to this disease, costing an estimated US$1.25 billion. Though fungicides can protect against these agroeconomic losses, application timing can be difficult to optimize because our understanding of tar spot dynamics is still evolving. The current view is that tar spot typically develops bottom-up through a repeating infection cycle. Because …

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A Machine Learning Model Of Perturb-Seq Data For Use In Space Flight Gene Expression Profile Analysis, Liam F. Johnson, James Casaletto, Lauren Sanders, Sylvain Costes

Graduate Industrial Research Symposium

The genetic perturbations caused by spaceflight on biological systems tend to have a system-wide effect which is often difficult to deconvolute it into individual signals with specific points of origin. Single cell multi-omic data can provide a profile of the perturbational effects, but does not necessarily indicate the initial point of interference within the network. The objective of this project is to take advantage of large scale and genome-wide perturbational datasets by using them to train a tuned machine learning model that is capable of predicting the effects of unseen perturbations in new data. Perturb-Seq datasets are large libraries of …

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The Impact Of Accessible Data On Cyberstalking, Elise Kwan

The Journal of Purdue Undergraduate Research

No abstract provided.

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Model Selection Through Cross-Validation For Supervised Learning Tasks With Manifold Data, Derek Brown

The Journal of Purdue Undergraduate Research

No abstract provided.

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Machine Learning Of Big Data: A Gaussian Regression Model To Predict The Spatiotemporal Distribution Of Ground Ozone, Jerry Gu

The Journal of Purdue Undergraduate Research

Tracking pollution levels on the ground is important to the environment and public health. One of the pollutants of concern is ozone, which, at high concentrations, can cause respiratory and cardiovascular problems. The National Center for Atmospheric Research (NCAR) has published valuable ozone data obtained from ground-based sensors installed at selected locations. Because it is unfeasible to measure the exact ozone levels everywhere at any time, it would be valuable to predict the temporal-spatial distributions of ozone concentration based on existing data. This would help us better understand the patterns and trends in the data and make better decisions to …

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A Computational Profile Of Invasive Lionfish In Belize: A New Insight On A Destructive Species, Joshua E. Balan

The Journal of Purdue Undergraduate Research

Since their discovery in the region in 2009, invasive Indonesian-native lionfish have been taking over the Belize Barrier Reef. As a result, populations of local species have dwindled as they are either eaten or outcompeted by the invaders. This has led to devastating losses ecologically and economically; massive industries in the local nations, such as fisheries and tourism, have suffered greatly. Attempting to combat this, local organizations, from nonprofits to ecotourism companies, have been manually spear-hunting them on scuba dives to cull the population. One such company, Reef Conservation Institute (ReefCI), operating out of Tom Owens Caye outside of Placencia, …

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Les Expositions Turnus, Une Page D’Histoire Transnationale Des Beaux-Arts En Suisse À La Fin Du Xixe Siècle. Et Comment Découvrir Les Humanités Numériques, Béatrice Joyeux-Prunel

Artl@s Bulletin

Cet article présente le travail de la classe d’introduction aux humanités numériques de l’Université de Genève sur les expositions Turnus en Suisse à partir des années 1840. Près de 50 catalogues ont été retranscrits, décrits et structurés à l’aide de scripts Python, puis géolocalisés. Les données ont été ajoutées à BasArt, le répertoire mondial de catalogues d’expositions d’Artl@s (https://artlas.huma-num.fr/map). Elles permettent de mieux comprendre les premières années de ces expositions et leurs dynamiques locales, fédérales et internationales. Le Turnus fut une plaque tournante pour les artistes suisses, voire un tremplin vers le marché européen de l’art.

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Deep Q-Learning Framework For Quantitative Climate Change Adaptation Policy For Florida Road Network Due To Extreme Precipitation, Orhun Aydin

I-GUIDE Forum

Climate change-induced extreme weather and increasing population are increasing the pressure on the global aging road networks. Adaptation requires designing interventions and alterations to the road networks that consider future dynamics of flooding and increased traffic due to the growing population. This paper introduces a reinforcement learning approach to designing interventions for Florida's road network under future traffic and climate projections. Three climate models and a tide and surge model are used to create flooding and coastal inundation projections, respectively. The optimal sequence of decisions for adapting Florida's road network to minimize flooding-related disruptions is solved by using a graph-based …

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Large-Scale Google Street View Images For Urban Change Detection, Fangzheng Lyu, Xinlin Ma, Yan Song, Eric Zhu, Shaowen Wang

I-GUIDE Forum

Urbanization has entered a new phase characterized by urban changes occurring at a micro-scale and “under the roof”, as opposed to external modifications. These changes, known as urban retrofitting, involve the incorporation of novel technologies or features into pre-existing systems to promote sustainability. Given the limitations of remote sensing images in identifying such urban changes, novel tools need to be developed for detecting urban retrofitting. In this study, we first build a pipeline to collect large-scale time-series urban street view images from Google Street View in Mecklenburg County, North Carolina. And we examine the feasibility of utilizing the acquired dataset …

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Graph Transformer Network For Flood Forecasting With Heterogeneous Covariates, Jimeng Shi, Vitalii Stebliankin, Zhaonan Wang, Shaowen Wang, Giri Narasimhan

I-GUIDE Forum

Floods can be very destructive causing heavy damage to life, property, and livelihoods. Global climate change and the consequent sea-level rise have increased the occurrence of extreme weather events, resulting in elevated and frequent flood risk. Therefore, accurate and timely flood forecasting in coastal river systems is critical to facilitate good flood management. However, the computational tools currently used are either slow or inaccurate. In this paper, we propose a Flood prediction tool using Graph Transformer Network (FloodGTN) for river systems. More specifically, FloodGTN learns the spatio-temporal dependencies of water levels at different monitoring stations using Graph Neural Networks (GNNs) …

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Cross-Scale Urban Land Cover Mapping: Empowering Classification Through Transfer Learning And Deep Learning Integration, Zhe Wang, Chao Fan, Xian Min, Shoukun Sun, Xiaogang Ma, Xiang Que

I-GUIDE Forum

Urban land cover mapping is essential for effective urban planning and resource management. Thanks to its ability to extract intricate features from urban datasets, deep learning has emerged as a powerful technique for urban classification. The U-net architecture has achieved state-of-the-art land cover classification performance, highlighting its potential for mapping urban trees at different spatial scales. However, deep learning approaches often require large, labeled datasets, which are challenging to acquire for specific urban contexts. Transfer learning addresses this limitation by leveraging pre-trained deep learning models on extensive datasets and adapting them to smaller urban datasets with limited labeled samples. Transfer …

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Solving Geospatial Problems Under Extreme Time Constraints: A Call For Inclusive Geocomputational Education, Coline C. Dony

I-GUIDE Forum

To prepare our next generation to face geospatial problems that have extreme time constraints (e.g., disasters, climate change) we need to create educational pathways that help students develop their geocomputational thinking skills. First, educators are central in helping us create those pathways, therefore, we need to clearly convey to them why and in which contexts this thinking is necessary. For that purpose, a new definition for geocomputational thinking is suggested that makes it clear that this thinking is needed for geospatial problems that have extreme time constraints. Secondly, we can not further burden educators with more demands, rather we should …

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Full-Text Articles in Data Science

Nci Research Impact And Expertise With Bibliometric Data, Senay Purzer, Wei Zakharov, Carla B. Zoltowski

Supplementary Content for Stewards of Data: A Practical Handbook for Undergraduate Researchers in Engineering and Applied Sciences

Statistical Analysis Of Log Transformation Effectiveness In Air Traffic Movement Forecasting During Covid-19 In South Africa, John Lehlaka Masekoameng

Journal of Aviation Technology and Engineering

Enhancing Education Through Virtual Reality: A Comparative Study Of Vr And Traditional Learning Environments, Shrivardhan Atluri

The Journal of Purdue Undergraduate Research

A Deep Learning Framework With Explainable Ai For Atmospheric Blocking Detection And Interpretation, Devansh Khandelwal

Discovery Undergraduate Interdisciplinary Research Internship

Crop Yield Prediction At Multiple Spatial Scales With Statistical Machine Learning, Vaibhav Charan, Pratishtha Poudel

Discovery Undergraduate Interdisciplinary Research Internship

Discovering And Designing Novel Perovskite Photovoltaic Materials Via Machine Learning, Junyeong Ahn

Discovery Undergraduate Interdisciplinary Research Internship

Mapping Responsible Workflows For Geospatial Data Science: Developing The I-Guide Data Ethics Toolkit, Peter T. Darch, Kyra M. Abrams, Ivan Y M Kong

I-GUIDE Forum

Machine Learning-Based Variance Analysis Of Brightness Temperature In Simulated Satellite Footprints, Chhaya R. Kulkarni, Nikki Prive, Vandana P. Janeja

I-GUIDE Forum

Application Of Physics-Informed Neural Networks On Crop Yield Prediction At Multiple Scales, Aditya P. Prabhu, Pratishtha Poudel, James V. Krogmeier

Discovery Undergraduate Interdisciplinary Research Internship

Where To Build Food Banks: A Machine Learning Approach, Gavin Ruan

The Journal of Purdue Undergraduate Research

Predictive Maintenance Analysis Of Turbofan Engine Sensor Data, Stanley A. Melkumian

The Journal of Purdue Undergraduate Research

Tradeoffs Of Generalization, Kyra M. Abrams, Peter T. Darch

I-GUIDE Forum

Smart Airports: Artificial Intelligence–Enabled Internet Of Things Networks Using Blockchain Technology, Edwin Ongola

Journal of Aviation Technology and Engineering

Geospatial Analysis Of Agricultural Potential In The United States, Diana Febrita

Graduate Industrial Research Symposium

Accuracy Of Nitrate Hysteresis And Flushing For Agricultural Watersheds In The Midwest, Noah Rudko, Sara K. W. Mcmillian, Jane Frankenberger, François Birgand

Graduate Industrial Research Symposium

Characterization Of Biological Particles Using An Integrated Hyperspectral Imaging And Machine Learning, Kaeul Lim, Arezoo Ardekani

Graduate Industrial Research Symposium

Sepsis Treatment: Reinforced Sequential Decision-Making For Saving Lives, Dipesh Tamboli, Jiayu Chen, Kiran Pranesh Jotheeswaran, Denny Yu, Vaneet Aggarwal

Graduate Industrial Research Symposium

Online Class-Incremental Learning For Real-World Food Image Classification, Siddeshwar Raghavan, Jiangpeng He, Fengqing Zhu

Graduate Industrial Research Symposium

Modelling The "Bottom-Up" Development Pattern Of Tar Spot Disease In Corn, Brenden Lane, Joaquín Guillermo Ramírez-Gil, Carlos Góngora-Canul, Mariela Sofia Fernandez Campos, Andres Cruz-Sancan, Fidel E. Jiménez-Beitia, Alex G. Acosta-Guatemal, Wily Sic, C. D. Cruz

Graduate Industrial Research Symposium

A Machine Learning Model Of Perturb-Seq Data For Use In Space Flight Gene Expression Profile Analysis, Liam F. Johnson, James Casaletto, Lauren Sanders, Sylvain Costes

Graduate Industrial Research Symposium

The Impact Of Accessible Data On Cyberstalking, Elise Kwan

The Journal of Purdue Undergraduate Research

Model Selection Through Cross-Validation For Supervised Learning Tasks With Manifold Data, Derek Brown

The Journal of Purdue Undergraduate Research

Machine Learning Of Big Data: A Gaussian Regression Model To Predict The Spatiotemporal Distribution Of Ground Ozone, Jerry Gu

The Journal of Purdue Undergraduate Research

A Computational Profile Of Invasive Lionfish In Belize: A New Insight On A Destructive Species, Joshua E. Balan

The Journal of Purdue Undergraduate Research

Les Expositions Turnus, Une Page D’Histoire Transnationale Des Beaux-Arts En Suisse À La Fin Du Xixe Siècle. Et Comment Découvrir Les Humanités Numériques, Béatrice Joyeux-Prunel

Artl@s Bulletin

Deep Q-Learning Framework For Quantitative Climate Change Adaptation Policy For Florida Road Network Due To Extreme Precipitation, Orhun Aydin

I-GUIDE Forum

Large-Scale Google Street View Images For Urban Change Detection, Fangzheng Lyu, Xinlin Ma, Yan Song, Eric Zhu, Shaowen Wang

I-GUIDE Forum

Graph Transformer Network For Flood Forecasting With Heterogeneous Covariates, Jimeng Shi, Vitalii Stebliankin, Zhaonan Wang, Shaowen Wang, Giri Narasimhan

I-GUIDE Forum

Cross-Scale Urban Land Cover Mapping: Empowering Classification Through Transfer Learning And Deep Learning Integration, Zhe Wang, Chao Fan, Xian Min, Shoukun Sun, Xiaogang Ma, Xiang Que

I-GUIDE Forum

Solving Geospatial Problems Under Extreme Time Constraints: A Call For Inclusive Geocomputational Education, Coline C. Dony

I-GUIDE Forum