Sampling Strategies based on Wisdom of Crowds for Amazon Deforestation Detection

• URL: http://arxiv.org/abs/2408.12381v1
• Date: Thu, 22 Aug 2024 13:21:47 GMT
• Title: Sampling Strategies based on Wisdom of Crowds for Amazon Deforestation Detection
• Authors: Hugo Resende, Eduardo B. Neto, Fabio A. M. Cappabianco, Alvaro L. Fazenda, Fabio A. Faria,
• Abstract summary: In April 2019, a project based on Citizen Science and Machine Learning models called ForestEyes (FE) was launched. FE provides supplementary data to assist experts from government and non-profit organizations in their deforestation monitoring efforts. Recent research has shown that labeling FE project volunteers/citizen scientists helps tailor machine learning models.
• Score: 0.5825410941577593
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Conserving tropical forests is highly relevant socially and ecologically because of their critical role in the global ecosystem. However, the ongoing deforestation and degradation affect millions of hectares each year, necessitating government or private initiatives to ensure effective forest monitoring. In April 2019, a project based on Citizen Science and Machine Learning models called ForestEyes (FE) was launched with the aim of providing supplementary data to assist experts from government and non-profit organizations in their deforestation monitoring efforts. Recent research has shown that labeling FE project volunteers/citizen scientists helps tailor machine learning models. In this sense, we adopt the FE project to create different sampling strategies based on the wisdom of crowds to select the most suitable samples from the training set to learn an SVM technique and obtain better classification results in deforestation detection tasks. In our experiments, we can show that our strategy based on user entropy-increasing achieved the best classification results in the deforestation detection task when compared with the random sampling strategies, as well as, reducing the convergence time of the SVM technique.

Related papers

• Adaptive teachers for amortized samplers [76.88721198565861]
  Amortized inference is the task of training a parametric model, such as a neural network, to approximate a distribution with a given unnormalized density where exact sampling is intractable. Off-policy RL training facilitates the discovery of diverse, high-reward candidates, but existing methods still face challenges in efficient exploration. We propose an adaptive training distribution (the Teacher) to guide the training of the primary amortized sampler (the Student) by prioritizing high-loss regions.
  arXiv  Detail & Related papers  (2024-10-02T11:33:13Z)
• Enhancing Tree Type Detection in Forest Fire Risk Assessment: Multi-Stage Approach and Color Encoding with Forest Fire Risk Evaluation Framework for UAV Imagery [0.0]
  Forest fires pose a significant threat to ecosystems, economies, and human health worldwide. Unmanned Aerial Vehicles equipped with advanced computer vision algorithms offer a promising solution for forest fire detection and assessment. We optimize an integrated forest fire risk assessment framework using UAVs and multi-stage object detection algorithms.
  arXiv  Detail & Related papers  (2024-07-27T05:52:31Z)
• A Satellite Band Selection Framework for Amazon Forest Deforestation Detection Task [0.5825410941577593]
  Deforestation and degradation impact millions of hectares annually, necessitating government or private initiatives for effective forest monitoring. This study introduces a novel framework that employs the Univariate Marginal Distribution Algorithm (UMDA) to select spectral bands from Landsat-8 satellite. This selection guides a semantic segmentation architecture, DeepLabv3+, enhancing its performance.
  arXiv  Detail & Related papers  (2024-04-03T11:47:20Z)
• Pre-Training and Fine-Tuning Generative Flow Networks [61.90529626590415]
  We introduce a novel approach for reward-free pre-training of GFlowNets. By framing the training as a self-supervised problem, we propose an outcome-conditioned GFlowNet that learns to explore the candidate space. We show that the pre-trained OC-GFN model can allow for a direct extraction of a policy capable of sampling from any new reward functions in downstream tasks.
  arXiv  Detail & Related papers  (2023-10-05T09:53:22Z)
• ForensicsForest Family: A Series of Multi-scale Hierarchical Cascade Forests for Detecting GAN-generated Faces [53.739014757621376]
  We describe a simple and effective forest-based method set called em ForensicsForest Family to detect GAN-generate faces. ForenscisForest is a newly proposed Multi-scale Hierarchical Cascade Forest. Hybrid ForensicsForest integrates the CNN layers into models. Divide-and-Conquer ForensicsForest can construct a forest model using only a portion of training samplings.
  arXiv  Detail & Related papers  (2023-08-02T06:41:19Z)
• ForestEyes Project: Conception, Enhancements, and Challenges [68.8204255655161]
  This work presents a Citizen Science project called ForestEyes. It uses volunteer's answers through the analysis and classification of remote sensing images to monitor deforestation regions in rainforests. To evaluate the quality of those answers, different campaigns/workflows were launched using remote sensing images from Brazilian Legal Amazon.
  arXiv  Detail & Related papers  (2022-08-24T17:48:12Z)
• Neuroevolution-based Classifiers for Deforestation Detection in Tropical Forests [62.997667081978825]
  Millions of hectares of tropical forests are lost every year due to deforestation or degradation. Monitoring and deforestation detection programs are in use, in addition to public policies for the prevention and punishment of criminals. This paper proposes the use of pattern classifiers based on neuroevolution technique (NEAT) in tropical forest deforestation detection tasks.
  arXiv  Detail & Related papers  (2022-08-23T16:04:12Z)
• Deep Learning Based 3D Point Cloud Regression for Estimating Forest Biomass [15.956463815168034]
  Knowledge of forest biomass stocks and their development is important for implementing effective climate change mitigation measures. Remote sensing using airborne LiDAR can be used to measure vegetation biomass at large scale. We present deep learning systems for predicting wood volume, above-ground biomass (AGB), and subsequently carbon directly from 3D LiDAR point cloud data.
  arXiv  Detail & Related papers  (2021-12-21T16:26:13Z)
• ForestNet: Classifying Drivers of Deforestation in Indonesia using Deep Learning on Satellite Imagery [10.924137779582814]
  We develop a deep learning model called ForestNet to classify the drivers of primary forest loss in Indonesia. Using satellite imagery, ForestNet identifies the direct drivers of deforestation in forest loss patches of any size.
  arXiv  Detail & Related papers  (2020-11-11T00:28:40Z)
• Leveraging traditional ecological knowledge in ecosystem restoration projects utilizing machine learning [77.34726150561087]
  Community engagement throughout the stages of ecosystem restoration projects could contribute to improved community well-being. We suggest that adaptive and scalable practices could incentivize interdisciplinary collaboration during all stages of ecosystemic ML restoration projects.
  arXiv  Detail & Related papers  (2020-06-22T16:17:48Z)

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.