Counting Cows: Tracking Illegal Cattle Ranching From High-Resolution Satellite Imagery

Cattle farming is responsible for 8.8\% of greenhouse gas emissions worldwide. In addition to the methane emitted due to their digestive process, the growing need for grazing areas is an important driver of deforestation. While some regulations are in place for preserving the Amazon against deforestation, these are being flouted in various ways, hence the need to scale and automate the monitoring of cattle ranching activities. Through a partnership with \textit{Global Witness}, we explore the feasibility of tracking and counting cattle at the continental scale from satellite imagery. With a license from Maxar Technologies, we obtained satellite imagery of the Amazon at 40cm resolution, and compiled a dataset of 903 images containing a total of 28498 cattle. Our experiments show promising results and highlight important directions for the next steps on both counting algorithms and the data collection process for solving such challenges. The code is available at \url{this https URL}.

[1]  Mark W. Schmidt,et al.  Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates , 2019, NeurIPS.

[2]  Mark W. Schmidt,et al.  Where are the Blobs: Counting by Localization with Point Supervision , 2018, ECCV.

[3]  P. Vitousek Beyond Global Warming: Ecology and Global Change , 1994 .

[4]  William Parker,et al.  A Weakly Supervised Consistency-based Learning Method for COVID-19 Segmentation in CT Images , 2020, 2021 IEEE Winter Conference on Applications of Computer Vision (WACV).

[5]  William Parker,et al.  A Weakly Supervised Region-Based Active Learning Method for COVID-19 Segmentation in CT Images , 2020, ArXiv.

[6]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Ben. G. Weinstein A computer vision for animal ecology. , 2018, The Journal of animal ecology.

[8]  Mostafa Rahimi Azghadi,et al.  Affinity LCFCN: Learning to Segment Fish with Weak Supervision , 2020, ArXiv.

[9]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[10]  B. Young,et al.  Widespread amphibian extinctions from epidemic disease driven by global warming , 2006, Nature.

[11]  H. Steinfeld,et al.  Tackling climate change through livestock : a global assessment of emissions and mitigation opportunities , 2013 .

[12]  Barry W. Brook,et al.  Catastrophic extinctions follow deforestation in Singapore , 2003, Nature.

[13]  Yoshua Bengio,et al.  Count-ception: Counting by Fully Convolutional Redundant Counting , 2017, 2017 IEEE International Conference on Computer Vision Workshops (ICCVW).

[14]  Issam H. Laradji,et al.  A realistic fish-habitat dataset to evaluate algorithms for underwater visual analysis , 2020, Scientific Reports.

[15]  Luciano Vieira Koenigkan,et al.  Counting Cattle in UAV Images—Dealing with Clustered Animals and Animal/Background Contrast Changes , 2020, Sensors.

[16]  Cristiana Santos,et al.  Satellite Imagery, Very High-Resolution and Processing-Intensive Image Analysis: Potential Risks Under the GDPR , 2019, Air and Space Law.

[17]  Saturnino Maldonado-Bascón,et al.  Extremely Overlapping Vehicle Counting , 2015, IbPRIA.

[18]  Andrew Zisserman,et al.  Learning To Count Objects in Images , 2010, NIPS.

[19]  David Moyer,et al.  Assessing Rotation-Invariant Feature Classification for Automated Wildebeest Population Counts , 2016, PloS one.

[20]  Ramprasaath R. Selvaraju,et al.  Counting Everyday Objects in Everyday Scenes , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  Yuhong Li,et al.  CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[22]  Lian Pin Koh,et al.  Momentum Drives the Crash: Mass Extinction in the Tropics 1 , 2006 .

[23]  Issam H. Laradji,et al.  Looc: Localize Overlapping Objects with Count Supervision , 2020, 2020 IEEE International Conference on Image Processing (ICIP).

[24]  Juan M. Corchado,et al.  Detection of Cattle Using Drones and Convolutional Neural Networks , 2018, Sensors.

[25]  Andrew K. Skidmore,et al.  Automatic Counting of Large Mammals from Very High Resolution Panchromatic Satellite Imagery , 2017, Remote. Sens..

[26]  Huping Ye,et al.  Integrating satellite and unmanned aircraft system (UAS) imagery to model livestock population dynamics in the Longbao Wetland National Nature Reserve, China. , 2020, The Science of the total environment.

[27]  Sharan Vaswani,et al.  Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence , 2020, AISTATS.

[28]  Maryam Rahnemoonfar,et al.  DisCountNet: Discriminating and Counting Network for Real-Time Counting and Localization of Sparse Objects in High-Resolution UAV Imagery , 2019, Remote. Sens..

[29]  Luciano Vieira Koenigkan,et al.  A Study on the Detection of Cattle in UAV Images Using Deep Learning , 2019, Sensors.

[30]  Mark W. Schmidt,et al.  Where are the Masks: Instance Segmentation with Image-level Supervision , 2019, BMVC.

[31]  Shaodi You,et al.  Cattle detection and counting in UAV images based on convolutional neural networks , 2019, International Journal of Remote Sensing.

[32]  Andrew Zisserman,et al.  Counting in the Wild , 2016, ECCV.