Methane emissions from oil and gas infrastructure, wetlands, and livestock contribute to the greenhouse gas inventory. The analysis of satellite short-wave infrared imagery offers opportunities for screening large areas to detect methane leaks. Deep learning algorithms excel at analyzing these data, however, they require large annotated datasets for model calibration that are difficult to get. To overcome this limitation, we explore a methodology to spot methane plumes using deep binary classifiers trained on a large dataset of synthetically created methane plumes, customized for this specific task, using publicly available images of the Sentine1-2 satellites. To build the database, we simulate plume patterns using the Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT) and use a simple stochastic model to account for reflectance attenuation due to methane in band 12 centered at 2190 nm. To help distinguish methane plumes from the image background, we compute a methane signature image based on a background subtraction technique. Once calibrated, the classification model is applied to image patches centered in the local minima of the methane signature within the satellite image, scoring a value ranging from 0 to 1 associated with the presence of a methane plume. We compare experimentally the general-purpose ResNet architecture and MethaNet, a domain-specific convolutional neural network, using simulated data. Then, we evaluate the feasibility of our approach in detecting large methane leaks at two study sites located in the Hassi Messaoud oil field in Algeria and the Permian Basin in the US, each covering an area of 0.25$\times$ 0.25 degrees. We found that ResNet is effective in identifying large, known methane plumes that were set aside for testing purposes. This method could be considered as a component of a solution for planning mitigation activities.

Inferring the source information of greenhouse gases, such as methane, from spatially sparse sensor observations is an essential element in mitigating climate change. While it is well understood that the complex behavior of the atmospheric dispersion of such pollutants is governed by the Advection-Diffusion equation, it is difficult to directly apply the governing equations to identify the source location and magnitude (inverse problem) because of the spatially sparse and noisy observations, i.e., the pollution concentration is known only at the sensor locations and sensors sensitivity is limited. Here, we develop a multi-task learning framework that can provide high-fidelity reconstruction of the concentration field and identify emission characteristics of the pollution sources such as their location, emission strength, etc. from sparse sensor observations. We demonstrate that our proposed framework is able to achieve accurate reconstruction of the methane concentrations from sparse sensor measurements as well as precisely pin-point the location and emission strength of these pollution sources.

For large scale monitoring of the environment, the number of possible pollution sources can be larger than the number of sensors. For optimal sensor placement under various wind fields in source inversion problems, this paper proposes a framework under non-Gaussian priors for the detection and inversion estimate of emission rates. The optimization framework with non-Gaussian prior utilizes a bi-level optimization expression with inner quadratic programming. The proposed truncated Gaussian prior is to incorporate non-negativity of emission rates, but it poses a challenge in optimization. We preliminarily investigate the bi-level optimization with a Gaussian plume model example. The Karush–Kuhn–Tucker conditions of the inner quadratic programming are considered for solving the bi-level optimization. The efficiency of the proposed optimization framework is demonstrated by numerical results to optimally place sensors and quantify emission rates.

Source localization and emission strength quantification is an ongoing challenge for distributed pollution sources. Here we outline a wireless sensor approach to localize all potential emission sources on an oil and gas well pad under well controlled experimental conditions. Using backtracking algorithms and time synchronized methane and wind measurements, sources are attributed to equipment on the well pad. After localizing the sources, we estimate source magnitude and uncertainty using a Bayesian inference method. The approach outlined in this work can identify and quantify leaks in the close proximity of the sources under dynamic plume dispersion taking into account the site layout, potential source locations and the characteristics of the sensor network. Localization of the system is within a meter from the emission location and the Bayesian approach yields rates that are within a factor 3 of the actual rate. Further, the actual rates are generally within the 95% confidence intervals for the prediction.

Nature-based carbon sequestration is currently the most viable solutions to extract CO2from the atmosphere and convert it into carbon. Oceans, soils and forests have the potential to capture and store large amount of carbon for decades. There is an ongoing debate about the permanence of the carbon sequestered by nature-based processes and the precise techniques required to monitor these carbon pools. Remote sensing plays a crucial role in the large scale observations of the Earth surface and provides a scalable method to monitor land use that can affect carbon sequestration. Optical spectral information and radar signals are the best candidates as proxy data to quantify and monitor the change in carbon sequestered. Here we outline the design of an AI enabled framework to monitor, verify, and quantify carbon sequestration in nature-based carbon sequestration processes.

A key challenge of supervised learning is the availability of human-labeled data. We evaluate a big data processing pipeline to auto-generate labels for remote sensing data. It is based on rasterized statistical features extracted from surveys such as e.g. LiDAR measurements. Using simple combinations of the rasterized statistical layers, it is demonstrated that multiple classes can be generated at accuracies of ~ 0.9.As proof of concept, we utilize the big geo-data platform IBM PAIRS to dynamically generate such labels in dense urban areas with multiple land cover classes. The general method proposed here is platform independent, and it can be adapted to generate labels for other satellite modalities in order to enable machine learning on overhead imagery for land use classification and object detection.

An automated machine learning framework for geospatial data named PAIRS AutoGeo is introduced on IBM PAIRS Geoscope big data and analytics platform. The frame-work simplifies the development of industrial machine learning solutions leveraging geospatial data to the extent that the user inputs are minimized to merely a text file containing labeled GPS coordinates. PAIRS AutoGeo automatically gathers required data at the location coordinates, assembles the training data, performs quality check, and trains multiple machine learning models for subsequent deployment. The framework is validated using a realistic industrial use case of tree species classification. Open-source tree species data are used as the input to train a random forest classifier and a modified ResNet model for 10-way tree species classification based on aerial imagery, which leads to an accuracy of 59.8% and 81.4%, respectively. This use case exemplifies how PAIRS AutoGeo enables users to leverage machine learning without extensive geospatial expertise.

The assessment of the vegetation growth rate given remote sensing data is a challenging task in the Earth Observation sciences. LiDAR data acquisition is commonly used to extract height information at a given moment in time, however, the associated cost and complexity restrict continuous acquisitions. Frequently captured aerial imagery can be used to identify and separate vegetation from bare land, water, impervious surface, or built infrastructure. A combination of LiDAR data with aerial and radar imagery allows to track dynamic seasonal growth of vegetation around critical infrastructure such as power lines. We present a general framework that integrates tree identification and growth assessment around power lines with the goal to identify locations of high risk where trees potentially cause power outages.

Large scale environmental monitoring requires dynamic optimization of data transmission, power management, and distribution of the computational load. In this work, we demonstrate the use of a wireless sensor network for detection of chemical leaks on gas oil well pads. The sensor network consist of chemi-resistive and wind sensors and aggregates all the data and transmits it to the cloud for further analytics processing. The sensor network data is integrated with an inversion model to identify leak location and quantify leak rates. We characterize the sensitivity and accuracy of such system under multiple well controlled methane release experiments. It is demonstrated that even 1 hour measurement with 10 sensors localizes leaks within 1 m and determines leak rate with an accuracy of 40%. This integrated sensing and analytics solution is currently refined to be a robust system for long term remote monitoring of methane leaks, generation of alarms, and tracking regulatory compliance.

IBM's Physical Analytics Integrated Data Repository and Services (PAIRS) is a geospatial Big Data service. PAIRS contains a massive amount of curated geospatial (or more precisely spatio-temporal) data from a large number of public and private data resources, and also supports user contributed data layers. PAIRS offers an easy-to-use platform for both rapid assembly and retrieval of geospatial datasets or performing complex analytics, lowering time-to-discovery significantly by reducing the data curation and management burden. In this paper, we review recent progress with PAIRS and showcase a few exemplary analytical applications which the authors are able to build with relative ease leveraging this technology.

Geospatial data volume exceeds hundreds of Petabytes and is increasing exponentially mainly driven by images/videos/data generated by mobile devices and high resolution imaging systems. Fast data discovery on historical archives and/or real time datasets is currently limited by various data formats that have different projections and spatial resolution, requiring extensive data processing before analytics can be carried out. A new platform called Physical Analytics Integrated Repository and Services (PAIRS) is presented that enables rapid data discovery by automatically updating, joining, and homogenizing data layers in space and time. Built on top of open source big data software, PAIRS manages automatic data download, data curation, and scalable storage while being simultaneously a computational platform for running physical and statistical models on the curated datasets. By addressing data curation before data being uploaded to the platform, multi-layer queries and filtering can be performed in real time. In addition, PAIRS offers a foundation for developing custom analytics. Towards that end we present two examples with models which are running operationally: (1) high resolution evapo-transpiration and vegetation monitoring for agriculture and (2) hyperlocal weather forecasting driven by machine learning for renewable energy forecasting.