Facilitating human-wildlife cohabitation through conflict prediction

• URL: http://arxiv.org/abs/2109.10637v1
• Date: Wed, 22 Sep 2021 10:30:06 GMT
• Title: Facilitating human-wildlife cohabitation through conflict prediction
• Authors: Susobhan Ghosh, Pradeep Varakantham, Aniket Bhatkhande, Tamanna Ahmad, Anish Andheria, Wenjun Li, Aparna Taneja, Divy Thakkar, Milind Tambe
• Abstract summary: We do an illustrative case study on human-wildlife conflicts in the Bramhapuri Forest Division in Chandrapur, Maharashtra, India. This is the first effort at prediction of human-wildlife conflicts in unprotected areas and using those predictions for deploying interventions on the ground.
• Score: 28.51258139168738
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With increasing world population and expanded use of forests as cohabited regions, interactions and conflicts with wildlife are increasing, leading to large-scale loss of lives (animal and human) and livelihoods (economic). While community knowledge is valuable, forest officials and conservation organisations can greatly benefit from predictive analysis of human-wildlife conflict, leading to targeted interventions that can potentially help save lives and livelihoods. However, the problem of prediction is a complex socio-technical problem in the context of limited data in low-resource regions. Identifying the "right" features to make accurate predictions of conflicts at the required spatial granularity using a sparse conflict training dataset} is the key challenge that we address in this paper. Specifically, we do an illustrative case study on human-wildlife conflicts in the Bramhapuri Forest Division in Chandrapur, Maharashtra, India. Most existing work has considered human-wildlife conflicts in protected areas and to the best of our knowledge, this is the first effort at prediction of human-wildlife conflicts in unprotected areas and using those predictions for deploying interventions on the ground.

Related papers

• Conflicts in Texts: Data, Implications and Challenges [58.03478157713084]
  Conflicts could reflect the complexity of situations, changes that need to be explained and dealt with, difficulties in data annotation, and mistakes in generated outputs. This survey categorizes these conflicts into three key areas: (1) natural texts on the web, where factual inconsistencies, subjective biases, and multiple perspectives introduce contradictions; (2) human-annotated data, where annotator disagreements, mistakes, and societal biases impact model training; and (3) model interactions, where hallucinations and knowledge conflicts emerge during deployment. We highlight key challenges and future directions for developing conflict-aware NLP systems that can reason over and reconcile conflicting information more effectively
  arXiv  Detail & Related papers  (2025-04-28T04:24:01Z)
• Intelligent Bear Prevention System Based on Computer Vision: An Approach to Reduce Human-Bear Conflicts in the Tibetan Plateau Area, China [7.9835451566800195]
  Conflicts between humans and bears on the Tibetan Plateau present substantial threats to local communities. This research introduces a novel strategy that incorporates computer vision alongside Internet of Things (IoT) technologies to alleviate these issues.
  arXiv  Detail & Related papers  (2025-03-29T18:10:11Z)
• Building Damage Assessment in Conflict Zones: A Deep Learning Approach Using Geospatial Sub-Meter Resolution Data [9.146968506196446]
  We build an annotated dataset with pre- and post-conflict images of the Ukrainian city of Mariupol. We then explore the transferability of the CNN models in both zero-shot and learning scenarios. This is the first study to use sub-meter resolution imagery to assess building damage in combat zones.
  arXiv  Detail & Related papers  (2024-10-07T07:26:38Z)
• Harnessing Artificial Intelligence for Wildlife Conservation [0.0937465283958018]
  Conservation AI detects and classifies animals, humans, and poaching-related objects using visual spectrum and thermal infrared cameras. The platform processes this data with convolutional neural networks (CNNs) and Transformer architectures to monitor species. Case studies from Europe, North America, Africa, and Southeast Asia highlight the platform's success in species identification, biodiversity monitoring, and poaching prevention.
  arXiv  Detail & Related papers  (2024-08-30T09:13:31Z)
• Performative Prediction on Games and Mechanism Design [69.7933059664256]
  We study a collective risk dilemma where agents decide whether to trust predictions based on past accuracy. As predictions shape collective outcomes, social welfare arises naturally as a metric of concern. We show how to achieve better trade-offs and use them for mechanism design.
  arXiv  Detail & Related papers  (2024-08-09T16:03:44Z)
• Climate Change Impact on Agricultural Land Suitability: An Interpretable Machine Learning-Based Eurasia Case Study [94.07737890568644]
  As of 2021, approximately 828 million people worldwide are experiencing hunger and malnutrition. Climate change significantly impacts agricultural land suitability, potentially leading to severe food shortages. Our study focuses on Central Eurasia, a region burdened with economic and social challenges.
  arXiv  Detail & Related papers  (2023-10-24T15:15:28Z)
• Causal Discovery of Dynamic Models for Predicting Human Spatial Interactions [5.742409080817885]
  We propose an application of causal discovery methods to model human-robot spatial interactions. New methods and practical solutions are discussed to exploit, for the first time, a state-of-the-art causal discovery algorithm.
  arXiv  Detail & Related papers  (2022-10-29T08:56:48Z)
• Understanding Interpersonal Conflict Types and their Impact on Perception Classification [7.907976678407914]
  We use a novel annotation scheme and release a new dataset of situations and conflict aspect annotations. We then build a classifier to predict whether someone will perceive the actions of one individual as right or wrong in a given situation. Our findings have important implications for understanding conflict and social norms.
  arXiv  Detail & Related papers  (2022-08-18T10:39:35Z)
• Ranked Prioritization of Groups in Combinatorial Bandit Allocation [62.24280332575472]
  We propose a novel bandit objective that trades off between reward over species. We show this objective can be expressed as a weighted linear sum of Lipschitz-continuous reward functions.
  arXiv  Detail & Related papers  (2022-05-11T17:40:29Z)
• Didn't see that coming: a survey on non-verbal social human behavior forecasting [47.99589136455976]
  Non-verbal social human behavior forecasting has increasingly attracted the interest of the research community in recent years. Its direct applications to human-robot interaction and socially-aware human motion generation make it a very attractive field. We define the behavior forecasting problem for multiple interactive agents in a generic way that aims at unifying the fields of social signals prediction and human motion forecasting.
  arXiv  Detail & Related papers  (2022-03-04T18:25:30Z)
• Inter and Intra-Annual Spatio-Temporal Variability of Habitat Suitability for Asian Elephants in India: A Random Forest Model-based Analysis [1.370633147306388]
  We develop a Random Forest model to estimate the species distribution of Asian elephants in India. We observe that seasonal reduction in the suitable habitat may explain the patterns of Asian elephants and the increasing human-elephant conflict.
  arXiv  Detail & Related papers  (2021-07-22T06:42:54Z)
• From Static to Dynamic Prediction: Wildfire Risk Assessment Based on Multiple Environmental Factors [69.9674326582747]
  Wildfire is one of the biggest disasters that frequently occurs on the west coast of the United States. We propose static and dynamic prediction models to analyze and assess the areas with high wildfire risks in California.
  arXiv  Detail & Related papers  (2021-03-14T17:56:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.