Remote Sensing meets Deep Learning: Exploiting Spatio-Temporal-Spectral Satellite Images for Early Wildfire Detection

Wildfires are getting more severe and destructive. Due to their fast-spreading nature, wildfires are often detected when already beyond control and consequently cause billionscale effects in a very short time. Governments are looking for remote sensing methods for early wildfire detection, avoiding billion-dollar losses of damaged properties. The aim of this study was to develop an autonomous and intelligent system built on top of imagery data streams, which is available from around-the-clock satellites, to monitor and prevent fire hazards from becoming disasters. However, satellite data pose unique challenges for image processing techniques, including temporal dependencies across time steps, the complexity of spectral channels, and adversarial conditions such as cloud and illumination. In this paper, we propose a novel wildfire detection method that utilises satellite images in an advanced deep learning architecture for locating wildfires at pixel level. The detection outputs are further visualised in an interactive dashboard that allows wildfire mitigation specialists to deeply analyse regions of interest in the world-map. Our system is built and tested on the Geostationary Operational Environmental Satellites (GOES-16) streaming data source. Empirical evaluations show the superior performance of our approach over the baselines with 94% F1score and 1.5 times faster detections as well as its robustness against different types of wildfires and adversarial conditions.

[1]  Jun Jo,et al.  Detection and Classification of Vehicle Types from Moving Backgrounds , 2017, RiTA.

[2]  Steven Verstockt,et al.  Review of wildfire detection using social media , 2014 .

[3]  D. Roberts,et al.  WILDFIRE DETECTION FOR RETRIEVING FIRE TEMPERATURE FROM HYPERSPECTRAL DATA , 2008 .

[4]  Lorenzo Rosasco,et al.  Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review , 2016, International Journal of Automation and Computing.

[5]  Karl Aberer,et al.  An Evaluation of Aggregation Techniques in Crowdsourcing , 2013, WISE.

[6]  Yang Wang,et al.  SPTF: A Scalable Probabilistic Tensor Factorization Model for Semantic-Aware Behavior Prediction , 2017, 2017 IEEE International Conference on Data Mining (ICDM).

[7]  Marta C. González,et al.  Using Convolutional Networks and Satellite Imagery to Identify Patterns in Urban Environments at a Large Scale , 2017, KDD.

[8]  Siqi Ren,et al.  Multi-object Tracking with Pre-classified Detection , 2017, RiTA.

[9]  Karl Aberer,et al.  Answer validation for generic crowdsourcing tasks with minimal efforts , 2017, The VLDB Journal.

[10]  Zi Huang,et al.  Joint Event-Partner Recommendation in Event-Based Social Networks , 2018, 2018 IEEE 34th International Conference on Data Engineering (ICDE).

[11]  Hao Wang,et al.  Adapting to User Interest Drift for POI Recommendation , 2016, IEEE Transactions on Knowledge and Data Engineering.

[12]  G. Roberts,et al.  New GOES imager algorithms for cloud and active fire detection and fire radiative power assessment across North, South and Central America , 2010 .

[13]  Susan Pascoe,et al.  Interim report: 2009 Victorian Bushfires Royal Commission , 2009 .

[14]  Liang Chen,et al.  Mobi-SAGE: A Sparse Additive Generative Model for Mobile App Recommendation , 2017, 2017 IEEE 33rd International Conference on Data Engineering (ICDE).

[15]  Sabira Khatun,et al.  The Classification of EEG Signal Using Different Machine Learning Techniques for BCI Application , 2018, RiTA.

[16]  Shazia Wasim Sadiq,et al.  Discovering interpretable geo-social communities for user behavior prediction , 2016, 2016 IEEE 32nd International Conference on Data Engineering (ICDE).

[17]  C. Justice,et al.  The collection 6 MODIS active fire detection algorithm and fire products , 2016, Remote sensing of environment.

[18]  Taesup Moon,et al.  UDLR Convolutional Network for Adaptive Image Denoiser , 2018, RiTA.

[19]  Kai Yang,et al.  A Deep Active Survival Analysis Approach for Precision Treatment Recommendations: Application of Prostate Cancer , 2018, Expert Syst. Appl..

[20]  David R. Easterling,et al.  Impacts, Risks, and Adaptation in the United States: Fourth National Climate Assessment, Volume II , 2017 .

[21]  Catarina Eloy,et al.  Classification of breast cancer histology images using Convolutional Neural Networks , 2017, PloS one.

[22]  Matthias Weidlich,et al.  What-If Analysis with Conflicting Goals: Recommending Data Ranges for Exploration , 2018, 2018 IEEE 34th International Conference on Data Engineering (ICDE).

[23]  Peng Liu,et al.  Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing 1 Active Deep Learning for Classification of Hyperspectral Images , 2022 .

[24]  Zi Huang,et al.  Streaming Ranking Based Recommender Systems , 2018, SIGIR.

[25]  Matthias Weidlich,et al.  Retaining Data from Streams of Social Platforms with Minimal Regret , 2017, IJCAI.

[26]  Hyun Myung,et al.  Accurate Localization in Urban Environments Using Fault Detection of GPS and Multi-sensor Fusion , 2015, RiTA.

[27]  Andrew K. Skidmore,et al.  Advances in active fire detection using a multi-temporal method for next-generation geostationary satellite data , 2018, Int. J. Digit. Earth.

[28]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[29]  Hong Liu,et al.  A two-step convolutional neural network based computer-aided detection scheme for automatically segmenting adipose tissue volume depicting on CT images , 2017, Comput. Methods Programs Biomed..

[30]  Donald J. Wuebbles,et al.  Climate Science Special Report: Fourth National Climate Assessment, Volume I , 2017 .

[31]  W. Schroeder,et al.  The New VIIRS 375 m active fire detection data product: Algorithm description and initial assessment , 2014 .

[32]  Matthias Weidlich,et al.  Computing Crowd Consensus with Partial Agreement , 2018, IEEE Transactions on Knowledge and Data Engineering.

[33]  Shanjun Mao,et al.  Spectral–spatial classification of hyperspectral images using deep convolutional neural networks , 2015 .

[34]  Mourad Ouzzani,et al.  Data Curation with Deep Learning [Vision]: Towards Self Driving Data Curation , 2018, ArXiv.

[35]  Sadasivan Puthusserypady,et al.  A deep learning approach for real-time detection of atrial fibrillation , 2019, Expert Syst. Appl..

[36]  Karl Aberer,et al.  An Evaluation of Model-Based Approaches to Sensor Data Compression , 2013, IEEE Transactions on Knowledge and Data Engineering.

[37]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[38]  Karl Aberer,et al.  Minimizing Efforts in Validating Crowd Answers , 2015, SIGMOD Conference.

[39]  Yoram J. Kaufman,et al.  A Review of AVHRR-based Active Fire Detection Algorithms: Principles, Limitations, and Recommendations , 2000 .