ACM SIGSPATIAL International Workshop

Spatio-temporal Data Analysis for Wildlife Conservation (GeoWildLife 2023).

Nov 13, 2023. Hamburg, Germany. 08:00-12:00

To be held in 31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2023)

About the Workshop

In collaboration with ACM SIGSPATIAL, we are pleased to announce the call for papers for GeoWildLife 2023, a workshop dedicated to bridging the gap between AI-enabled spatio-temporal data analytics and wildlife conservation.

The primary objective of this workshop is to advance the state-of-the-art in AI-driven spatio-temporal data analysis for wildlife conservation. By connecting computer scientists, geospatial scientists, ecologists, and conservation practitioners, the workshop seeks to promote interdisciplinary collaboration and drive real-world impact. Through a series of keynote presentations, panel discussions, and interactive sessions, participants will explore various topics including remote sensing technologies, predictive modeling, movement ecology, species distribution modeling, habitat quality assessment, and mitigating human-wildlife conflict. Special focus will be given to ethically and responsibly harnessing AI to ensure the sustainability of conservation efforts.

Accepted papers will be included in the workshop proceedings, which will be published in the ACM Digital Library. At least one author of each accepted paper must register for the workshop and present the paper. We also offer authors the option to opt out of the proceedings. Such papers will be published on the workshop’s website and will not be considered archival for resubmission purposes.

Call for Papers

Topics of interest include but are not limited to:

  • Remote Sensing, UAV Imagery, and Wildlife Monitoring: AI interpretation of remote sensing data for tracking animal populations, identifying habitats, and monitoring ecosystem health. This includes techniques such as deep learning-based spatial data analysis.
  • Predictive Modeling and Mobility Simulations for Conservation: AI-driven models to predict species movements, population changes, and the effects of environmental changes on wildlife, leveraging high-performance spatio-temporal mobility simulation systems.
  • Movement Ecology, Urban Mobility, and AI: Using AI to understand species behavior and movement patterns based on spatial and temporal data in both natural and urban environments.
  • Species Distribution and Semantic Trajectory Modeling: Applications of AI in predicting and mapping the spatial distribution of different species under changing climate scenarios and leveraging generative models for semantic trajectory analysis.
  • AI in Habitat Quality Assessment and Urban Landscape Analysis: Employing AI algorithms to analyze, interpret spatial data, and evaluate urban habitats using augmented street-level imagery and points of interest.
  • Mitigating Human-Wildlife and Human-Urban Conflicts: Developing AI tools to predict and prevent conflicts between humans and wildlife and understanding spatio-temporal patterns in urban habitats.
  • Spatio-temporal Data Analytics for Movement Ecology, Wildlife Monitoring, and Natural Disaster Prediction: Integrating multi-output neural networks to predict natural phenomena like earthquakes, drought that could impact wildlife habitats.
  • Conservation Planning, Decision Support Systems, and Urban Infrastructure Mapping: Using AI to aid in spatial planning for conservation, identifying priority areas for protection, and mapping urban structures that might impact wildlife movement.
  • AI, Ethics, Data Privacy, and Wildlife Conservation: Delving into the ethical implications of using AI in wildlife conservation, including concerns related to privacy, surveillance, data ownership, and location-based biases.
  • Socio-Political Implications of AI in Conservation and Urban Mobility: Understanding the socio-political dimensions of AI-based conservation strategies and their impact on urban landscapes and communities.
  • AI-driven Citizen Science, Crowd-sourced Data: Leveraging AI-enhanced citizen science approaches in wildlife monitoring, conservation efforts, and understanding spatio-temporal patterns through narrative techniques.
  • Urban Representation Learning and Urban Mobility Analysis in Conservation: Applying graph neural networks and deep learning techniques to understand urban economic statuses and their implications for wildlife movement.
  • Distributed Data Warehousing and Indexing for Wildlife Conservation: Techniques like efficient storage and indexing for handling massive datasets related to wildlife monitoring and urban landscapes.

Submission guidelines

We welcome different kinds of papers, such as, but not limited to:

  • Novel research papers
  • Demo papers
  • Dataset and Challenge papers
  • Work-in-progress papers
  • Visionary papers (white papers)
  • Review paper (Relevant work that has been previously published)
Authors should clearly indicate in their abstracts the kinds of submissions that the papers belong to, to help reviewers better understand their contributions. We also encourage authors to submit papers based on their previously submitted or recently published work (within the last year) for consideration in the poster session. This provides an opportunity to familiarize the community with noteworthy research that may not be entirely new and subsequently brainstorm new ideas.

All papers will be peer reviewed, single-blinded. Submissions must be in PDF, no more than 8 pages (long/ full) or 4 pages (short) or 2 pages (abstract/ poster) — and formatted according to the ACM camera-ready templates available at

Authors whose papers are accepted to the workshop will have the opportunity to participate in a spotlight and poster session, and some set will also be chosen for oral presentation.

Important Dates

  • Paper submission deadline: August 30, 2023 (AoE) September 26, 2023 (AoE)
  • Notification of acceptance: October 10, 2023
  • Camera-ready paper deadline: October 20, 2023 (AoE)
  • Workshop date: November 13, 2023, 08:00-12:00

  • Submission Link!

Program Schedule

(All times in CET)

Venue: Radisson Blu Hotel Hamburg (Congressplatz 2 D-20355 Hamburg) Room: Moskau

Online Participants: Zoom Link

8:00-8:05 AM

Opening remarks by GeoWildLife'23 Workshop Organizers

8:05-9:00 AM

Keynote talk by Dr. Bettina Wachter

Research scientist at the Department of Evolutionary Ecology

Topic: From conflict to co-existence: Evidence-based solutions for the farmer-cheetah conflict in Namibia

Abstract: Conflicts between humans and carnivores occur worldwide, particularly when humans own livestock. Developing sustainable solutions is a major challenge, especially for endangered carnivore species. Namibia is home to several endangered carnivore species, mainly cheetahs. While most of these carnivore species occur in national parks, 80% of the cheetah population lives on private farmland where farmers keep their cattle herds. Because cheetahs occasionally kill cattle calves, a farmer- cheetah conflict has existed for decades, with many cheetahs killed by farmers. In our long-term project, we used GPS data to show that cheetahs are not evenly distributed across the landscape, but maintain communication centers that are spatially dispersed. In the communication centers there is a high level of cheetah activity, making them risk areas for cattle calves. Using also acceleration data give us the opportunity to identify the behavior of cheetahs, including feeding site. Together with the farmers, we developed a sustainable solution to substantially reduce cattle losses through adapted cattle management away from the communication centers. This successful adaptation led to a reduction in the farmer-cheetah conflict and farmers killing fewer cheetahs.

Bio: Dr. Bettina Wachter is a behavioural ecologist and evolutionary biologist who works since nearly 30 years on carnivores in eastern and southern Africa. She studied biology at the Swiss Federal Institute of Technology (ETH) in Zurich and did her doctoral thesis on spotted hyenas in the Ngorongogo Crater in Tanzania at the Max-Planck Institute for Behavioural Ecology in Seewiesen and the University of Berne. Since 2001, she is a scientist at the Leibniz Institute for Zoo and Wildlife Research and the head of the Cheetah Research Project in Namibia. She and her team work on free-ranging cheetahs on farmland inhabited by livestock farmers. The team studies the spatial ecology and distribution of cheetahs and develops in close collaboration with the farmers evidence based solutions to mitigate the farmer-cheetah conflict. For this, they capture cheetahs with fully automated box traps and collect movement data with GPS collars. The team is keen to further develop field technologies and data analyses. The team is also interested in the reproduction and health of cheetahs because this is crucial to predict the population dynamics of the species. For a project overview see

09:00-09:15 AM

Paper Presentation: 10-12min, QA: 3-5min

Title: A Generative Trajectory Interpolation Method for Imputing Gaps in Wildlife Movement Data

Authors: Zijian Wan and Somayeh Dodge. University of California Santa Barbara, USA.

TL;DR: Despite the surge in wildlife movement data due to improved tracking technologies, many datasets contain missing records requiring interpolation. This study introduces a generative adversarial network (GAN) combined with long short-term memory (LSTM) layers to interpolate these gaps, addressing the often-overlooked uncertainty of movement data.

09:15-09:30 AM

Paper Presentation: 10-12min, QA: 3-5min

Title: Progress toward automated migratory waterfowl census using drones and deep learning

Authors: Rowan Converse*, Christopher Lippitt*, Grant Harris+, Steven Sesnie+, Matthew Butler+ and David Stewart+. University of New Mexico*, US Fish and Wildlife Service+

TL;DR: Wildlife managers are exploring the use of automated aerial imaging via unoccupied aerial systems (UAS) and deep learning models, specifically convolutional neural networks (CNN), for more efficient waterfowl census in US wildlife refuges. Utilizing crowdsourced annotations for training, the study has identified that a limited number of representative UAS images can effectively train the CNN, with future deployments planned for New Mexico and Texas.

09:30-09:45 AM

Paper Presentation: 10-12min, QA: 3-5min

Title: Towards large-scale spatio-temporal tracking of animal behavior in the wild

Authors: Pranav C. Khandelwal*, Eric Price*, Daniel I. Rubenstein+ and Aamir Ahmad*. Institute of Flight Mechanics and Controls, University of Stuttgart, Germany* and Department of Ecology and Evolutionary Biology, Princeton University, USA+.

TL;DR: Drones are increasingly used for tracking animals, but extracting biologically relevant data from the videos is challenging and time-consuming. This research introduces an easy-to-use workflow with a graphical interface for semi-automatic tracking and classification of animal behavior from aerial footage, streamlining the process for ecologists and conservationists.

09:45-10:00 AM

Paper Presentation: 10-12min, QA: 3-5min

Title: Pipeline for open AIS Data with filtering based on vessel class

Authors: Mirjam Bayer, Tabea Fry, Soren Dethlefsen and Daniyal Kazempour. University of Kiel, Germany.

TL;DR: This study addresses the challenge of deciphering valuable information from massive data streams of vessel activities, especially focusing on the Automatic Identification System (AIS) used for maritime conservation amidst declining fish populations. We introduce a pipeline that efficiently extracts and processes AIS message streams, with a demonstration on the Danish Marine Authority's data. The system can filter and clean the voluminous data, targeting specific vessel classes such as merchandise ships, fishing vessels, or passenger ships, and can further enhance the data with features like water depth and distance to shore.

10:00-10:15 AM

Paper Presentation: 10-12min, QA: 3-5min

Title: Automated Estimation of Distance to Animals in Images: Applications for Monitoring Wildlife Abundance

Authors: Blair Mirka*, Christopher Lippitt*, Grant Harris+, Rowan Converse*, Michael Gurule*, Steven Sesnie+, Matthew Butler+, David Stewart+ and Zoe Rossman*. University of New Mexico*, US Fish and Wildlife Service+

TL;DR:This study presents a streamlined method merging camera traps and distance sampling for precise animal abundance and spatial distribution assessments. Utilizing photogrammetric techniques, the proposed system automatically gauges the target's distance from camera trap images, achieving near-accurate measurements. While currently harnessing crowd-sourced image annotations, this approach sets the stage for full AI automation in detecting and gauging wildlife from camera captures.

10:15-10:30 AM

Paper Presentation: 10-12min, QA: 3-5min

Title: Temporal and spatial pattern of wildlife attacks on human in Chitwan National Park, Nepal

Authors: Saroj Kandel, Thakur Silwal and Shailendra Kumar Yadav. Tribhuvan University, Nepal.

TL;DR: The research analyzed wildlife attacks on humans in Chitwan National Park from 2009 to 2020, revealing an increasing annual trend in such incidents. Among several species, rhinos were most frequently involved in conflicts, while elephants and tigers posed the highest fatality risks to humans. These findings, indicating a higher attack concentration in winter and near human dwellings, are vital for enhancing wildlife management and conservation strategies.

10:30-11:00 AM

Tea Break

11:00-11:40 AM

Keynote talk by Dr. Randall Boone

Wildlife ecologist, Natural Resource Ecology Laboratory, Colorado State University & Founding professor in the Department of Ecosystem Science and Sustainability, Colorado State University.

Topic: Evolutionary programming of animal behaviors using agent-based modeling

Abstract: Agent-based modeling is a flexible and growing method of assessing hypotheses about the traits and behaviors of animals, including their movement, habitat selection, and niche dynamics. Through the process of abduction, we hypothesize how individuals may interact with each other and their environments and assess if those interactions will grow population-level patterns of interest. Through often simple rules attributed to individuals, complex emergent population responses may be seen. Among the approaches that use interacting agents is evolutionary computation, and within that field is evolutionary programming, where the relevant phenotypes of individuals are represented. Individuals compete to maximize scores on some objective function. Poorly performing individuals are removed, and the best performers reproduce related solutions with some mutation. Over time, the solution improves. I use this approach to simulate evolutionary pathways in real-world organisms. I will provide examples simulating the annual migration of wildebeest in the Serengeti and a means of incorporating biotic relationships in niche envelope modeling of future abundances under a changing climate.

Bio: Dr. Randall Boone has been with the Natural Resource Ecology Laboratory, Colorado State University for more than two decades and is a founding professor in the Department of Ecosystem Science and Sustainability. Randall is a wildlife ecologist with training from Oregon State University and the University of Maine. He received his PH.D. in Wildlife Ecology from the University of Maine in 1996. After completing graduate work at the University of Maine, he joined Colorado State University. His experience is diverse, with research in wildlife ecology, agent-based modeling, spatial analyses and GIS, ecosystem modeling, landscape ecology, database management, biogeographical relationships of birds and plants, species/habitat relationships, wildlife and pastoral livestock mobility.

11:40-11:55 AM

Paper Presentation: 10-12min, QA: 3-5min

Title: Smart Camera Traps: Enabling Energy-Efficient Edge-AI for Remote Monitoring of Wildlife

Authors: Dhruv Tulasi, Alys Granados, Prabath Gunawardane, Abhay Kashyap, Zara McDonald and Sunil Thulasidasan. Felidae Conservation Fund, USA.

TL;DR: This study introduces an energy-efficient enhancement to camera traps using edge intelligence, aiming for extended remote wildlife monitoring. Utilizing commercially available components, the system can function for months on a minimal battery and is currently deployed for wild felid monitoring in Northern California. This innovation results from a synergy of conservation initiatives, skilled teams, and an open-source "maker culture" approach.


Best Paper Announcement and Closing remarks by GeoWildLife'23 Workshop Organizers


Organizer 1

Prasenjit Mitra

Professor, The Pennsylvania State University, USA and Visiting Professor, Leibniz University, Hannover, Germany

Organizer 2

Bistra Dilkina

Associate Professor, Computer Science, Co-Director, Center for Artificial Intelligence in Society (CAIS), University of Southern California, USA

Organizer 4

Thomas Müller

Professor for Movement Ecology and Biodiversity Conservation, Goethe University and Senckenberg Biodiversity and Climate Research Centre, Frankfurt, Germany

Organizer 4

Shreya Ghosh

Postdoctoral Scholar
The Pennsylvania State University, USA


  • Sanjay Chawla, Research Director of QCRI’s Data Analytics department
  • Dan Morris, Research Scientist, Google AI for Nature and Society
  • Bing Pan, Professor of Commercial Recreation and Tourism, The Pennsylvania State University
  • Lily Xu, Ph.D. Student, Harvard University, USA
  • Saptarshi Sengupta, Ph.D. Student, College of IST, Pennsylvania State University, USA
  • Fei Fang, Assistant Professor of Computer Science at Carnegie Mellon University, Pennsylvania
  • Emmanuel Dufourq, AIMS-Canada Junior Research Chair
  • Johnson Kinyua, Associate Teaching Professor, College of IST, Pennsylvania State University, USA
  • Brendan Derrick Taff, Associate Professor, Recreation, Park, and Tourism Management, The Pennsylvania State University, USA
  • Edwin Sabuhoro, Assistant Professor,Recreation, Park, and Tourism Management, The Pennsylvania State University, USA
  • Gileard Minja, Mwenge Catholic University Tanzania
  • Derek Lee, The Pennsylvania State University, USA
  • Andrew Perrault, Assistant Professor, The Ohio State University, USA
  • Titus Enock Adhola, Lecturer, University of Nairobi, Kenya
  • Randall Boone, Assistant Professor, Colorado State University, USA
  • Lorène Jeantet, Postdoctoral Researcher, University of Stellenbosch, African Institute for Mathematical Sciences - AIMS South Africa