While deep machine learning approaches are getting pervasively used in remote sensing and modeling the earth, difficulties due to the size of satellite data are always pains for scientists in implementing such experiential software. We present a programming model for geospatial machine learning based on TorchGeo and PyTorch, which are getting the de fact standards in programming with PyTorch/Python.  TorchGeo is open-sourced and designed to make it simple for remote sensing experts to explore machine learning solutions. Our objective is to allow machine-learning programs using TorchGeo to scale leveraging proprietary high-performance computing (HPC) and multicloud HPC resources, from ones notebook. One of key technologies specifically needed in geospatial machine learning is the smart integration of peta-scale data services and data-distributed parallel frameworks.  We implement such a platform as a part of IBM Research Geospatial Discovery Network (GDN) and experiment segmentation tasks such as flood detection from satellite data to show its scalability.

How to cite: Tatsubori, M., Kimura, D., Moriyama, T., Simumba, N., and Ishikawa, T.: A Programming Model for Geospatial Machine-Learning with Scalability in Hybrid Multiclouds, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3441, https://doi.org/10.5194/egusphere-egu23-3441, 2023.

Every experiment starts with the data, which needs to be fine-tuned for the specific use-case. We call this "analysis ready data (ARD)". In some cases, for the sake of reusability and comparability, the specifications for ARD are well defined. In many other cases, however, the procedures are not yet mature enough to support standardisation. In Earth Observation (EO) field this is especially true, as the whole community is moving from (semi)manually analysing individual scenes, from the time there were any data barely available, to processing of time-series, now that Landsat and Sentinel made this possible. We are now even facing a problem where there is simply too much of data, with PBs of open and commercial imagery being readily available. With the data being distributed at different places (Copernicus Data Access Service for Sentinel, AWS for Landsat) the challenge is further magnified. Machine learning (ML) approach can address the challenge of shifting through data, but ML as well requires data to be pre-processed for purpose and made available at the place where ML is running. Therefore, it is essential to have facility, which can generate ARD data customised for the specific analysis' requirements.

Sentinel Hub (SH) is a satellite imagery processing service, which is capable of on-the-fly gridding, re-projection, re-scaling, mosaicking, compositing, orthorectification and other actions required, either for integration in web-applications, where pictures are mostly served, or in ML and similar analysis processes, where pixel values and statistics are essential. SH works with original satellite data files and does not require replication or pre-processing.  It uses cloud infrastructure and innovative methods to efficiently process and distribute data in a matter of seconds. Sentinel Hub gives access to a rich collection of satellite data including a full set of Sentinel satellites, Landsat collections, commercial VHR collections and other complimentary collections. It also provides an ability for users to onboard their own data in one of the standardised formats. Furthermore, the data located at different clouds, can be fused together in one single process, benefiting from the variability and volume of different sensors.

There are two main capabilities, which make SH especially fit for purpose of generating on-demand ARD data. First one is the support for user-provided processing scripts, which are a set of recipes on what should happen with the sensor data (band composites, indices, even simple neural networks combining available data). The second one is a set of processing orchestration options. There is a Process API for immediate, access to the pixel values. Statistical API is optimised for time-series analysis, which aggregates the data over specific area of interest and provides configurable statistics through time. And then there are asynchronous siblings of these services, which are fine-tuned for large scale processing - if one wants to prepare ML features for the entire continent or get time-series for millions of agriculture parcels.

We will present the technology behind the scenes, making the processing possible, as well as several use-cases, how one can efficiently make use the service in ML.

How to cite: Milcinski, G. and Kolaric, P.: Sentinel Hub - federated on-demand ARD generation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4160, https://doi.org/10.5194/egusphere-egu23-4160, 2023.

Fast response to natural catastrophe events is crucial in our fast changing world. Creating comprehensible solutions based on the Earth Observation (EO) and geospatial data is complex and requires combining multiple data sources and maintaining high level of configuration parameters.

In this talk we discuss the application of microservices architecture to tackle some of the issues inherent to building products based on EO and geospatial data. We will present how decomposing sophisticated algorithms into small services can help with continuous delivery, scaling and deployment of large, complex applications that can be reused for various products. This architecture enables reproducibility of analysis which is a crucial component for applying machine learning and automation into any EO based product. We will also address the additional complexity of creating a distributed system as well as high dependency on data consistency and availability.

How to cite: Sarna, K., Hiekkasaari, J., and Taajamo, J.: Microservice architecture to enable fast assessment of the NatCat events based on EO and geospatial data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11709, https://doi.org/10.5194/egusphere-egu23-11709, 2023.

Key variable of earth observation (EO) systems is precipitation, as indicated by the wide spectrum of applications that is involved (e.g., water resources and early warning systems for flood/drought events). During the last decade, the EO community has put significant research efforts towards the development of satellite-based precipitation products (SPPs), however, their deployment in real-world applications has not yet reached the full potential, despite their ever-growing availability, spatiotemporal coverage and resolution. This may be associated with the reluctancy of end-users to employ SPPs, either worrying about uncertainty and biases inherited in SPPs or even due to the existence of multiple SPPs, whose performance fluctuates across the globe, and thus making it difficult to select the most appropriate SPP (some sort of a choice paradox). To address this issue, this work targets the development of an explainable machine learning approach capable of integrating multiple satellite-based precipitation (P) and soil moisture (SM) products into a single precipitation product. Hence, in principle, to create a new dataset that optimally combines the properties of each individual satellite dataset (used as predictors), better matching the ground-based observations (used as predictand, i.e., reference dataset). The proposed approach is showcased via a benchmark dataset consisted of 1009 cells/locations around the world (Europe, USA, Australia and India), highlighting its robustness as well as its application capability which are independent of specific climatic regimes and local peculiarities.

How to cite: Tsoukalas, I., Kossieris, P., Brocca, L., Barbetta, S., Mosaffa, H., and Makropoulos, C.: Can machine learning help us to create improved and trustworthy satellite-based precipitation products?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13852, https://doi.org/10.5194/egusphere-egu23-13852, 2023.

Analysing EO data is a complex process, and solutions often require custom tailored algorithms. On top of that, in the EO domain most problems come with an additional challenge: How can the solution be applied on a large scale?

Within the H2020 project Global Earth Monitor (GEM) we have updated and extended eo-learn with additional functionalities that allow for new approaches to scalable and cost-effective Earth Observation data processing. We have tied it with the Sentinel Hub’s unified main data interface (Process API), the Data Cube processing engine for constructing analysis-ready adjustable data cubes using Batch Process API, and, finally, the Statistical API and Batch Statistical API to streamline access to spatio-temporally aggregated satellite data.

As part of GEM processing framework, we have built eo-grow which facilitates extraction of valuable information from satellite imagery. eo-grow tackles the issues of scalability by enabling coordination of clusters to run the EO workflows over large areas using  Ray. At the same time the framework provides reproducibility and traceability of the experiments using schemed input configurations and their validation.

In eo-grow a workflow based solution is wrapped into a pipeline object, which takes care of parametrization, logging, storage, multi-processing, data management and more. The pipeline object is configured via a well-defined schema allowing straightforward experimentation and scaling up: going to larger area of interest, running on different time interval, or tweak of any other pipeline parameter becomes just a matter of updating (json) configuration, which additionally serve as record of the experiment.

eo-grow library has been publicly released on GitHub: https://github.com/sentinel-hub/eo-grow. The documentation available in the repository provides the overview of the eo-grow general structure, its core objects, and instructions on installation and using eo-grow with command line interface. Additional repository, https://github.com/sentinel-hub/eo-grow-examples showcases eo-grow on a few use-cases.

In the presentation we will introduce the framework and showcase its usability on concrete examples. We will illustrate how eo-grow is used in large-scale research experiments, explain its role in reproducibility and show how the no-code approach and code reuse facilitate the productionalization of the workflows.

How to cite: Batič, M., Lukšič, Ž., and Milcinski, G.: eo-grow - Earth Observation framework for scaled-up processing in Python, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16802, https://doi.org/10.5194/egusphere-egu23-16802, 2023.

Earth observation (EO) data are critical for monitoring the state of planet Earth and can be helpful for various real-world applications [1]. Although numerous benchmark datasets have been released, there is no unified platform for developing and fairly comparing deep learning models on EO data [2]. For deep learning methods, the backbone networks, hyper-parameters, and training details are influential factors while comparing the performances.. However, existing works usually neglect these details and even evaluate the performance with different training/validation/test dataset splits. This makes it difficult to fairly and reliably compare different algorithms. In this study, we introduce the EarthNets platform, an open deep-learning platform for remote sensing and Earth observation. The platform is based on PyTorch [3] and TorchData. There are about ten different libraries, covering different tasks in remote sensing. Among them, Dataset4EO is designed as a standard and easy-to-use data-loading library, which can be used alone or together with other high-level libraries like RSI-Classification (for image classification), RSI-Detection (for object detection), RSI-Segmentation (for semantic segmentation), and so on. Two factors are considered for the design of the EarthNets platform: the first one is the decoupling between dataset loading and high-level EO tasks. As there are more than 400 RS datasets with different data modalities, research domains, and download links, efficient preparation of analysis-ready data can largely accelerate the research for the whole community. The other factor is to bring advances in machine learning to EO by providing new deep-learning models. The EarthNets platform provides a fair and consistent evaluation of deep learning methods on remote sensing and Earth observation data [4]. It also helps bring together the remote sensing and a larger machine-learning community. The platform, dataset collections are publicly available at https://earthnets.github.io.

[1] Zhu, Xiao Xiang, et al. "Deep learning in remote sensing: A comprehensive review and list of resources." IEEE Geoscience and Remote Sensing Magazine 5.4 (2017): 8-36.

[2]Long, Yang, et al. "On creating benchmark dataset for aerial image interpretation: Reviews, guidances, and million-aid." IEEE Journal of selected topics in applied earth observations and remote sensing 14 (2021): 4205-4230.

[3] Paszke, Adam, et al. "Pytorch: An imperative style, high-performance deep learning library." Advances in neural information processing systems 32 (2019).

[4] Xiong, Zhitong, et al. "EarthNets: Empowering AI in Earth observation." arXiv preprint arXiv:2210.04936 (2022).

How to cite: Xiong, Z. and Zhu, X. X.: EarthNets: An Open Deep Learning Platform for Earth Observation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3501, https://doi.org/10.5194/egusphere-egu23-3501, 2023.

Understanding and quantifying the risk of the physical impacts of climate change and their subsequent consequences have crucial importance in the changing climate for both businesses and society more widely. Historically, modelling workflows to assess such impacts have been bespoke and constrained by the data they can consume, the compute infrastructure, the expertise required to run them and the specific ways they are configured. Here we present, a cloud-native modelling framework for running geospatial models in a flexible, scalable, configurable, user-friendly manner. This enables models (physical or ML/AI) to be rapidly onboarded and composed into workflows. These workflows can be flexible, dynamic and extendable, running as for historical events, or as forecast ensembles, with varying data inputs, or extended to model impact in the real world (e.g. for example to infrastructure and populations). The framework supports the streamlined training and deployment of AI models, which can be seamlessly integrated with physical models to create hybrid workflows. We demonstrate the application and features of the framework for the examples of flooding and wildfire.

How to cite: Edwards, B., Fraccaro, P., Stoyanov, N., Jones, A., Butt, J., Kuehnert, J., Taylor, A., and Garikipati, B.: A flexible, scalable, cloud-native framework for geospatial modelling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7233, https://doi.org/10.5194/egusphere-egu23-7233, 2023.

In many parts of the world, reforestation is an ongoing activity, but due to the deforestation processes (e.g. the change in soil conditions, agricultural expansion, and infrastructure expansion such as urbanisation or road building), the success rate of replanting is far from sure; therefore, it is essential to:

• have a good idea of the pre-planting conditions at the location,
• monitor the growth,
• improve the growing conditions whenever possible, and
• adapt the site selection criteria

In the proposed method, it is not possible to change the site selections of already planted locations, but it is possible to monitor the selected location and check under which conditions the trees are growing best. 

Several data sources are identified to predict plant health and stress, first to establish a baseline and, from this baseline, project into the future (short and mid-term). We compute the main vegetation index (NDVI) from the high-resolution image data provided by Planet (through the NICFI Basemaps for Tropical Forest Monitoring program). The historical NDVI values are obtained from the Sentinel 2 (and potentially LandSat) data at lower resolutions. Environmental conditions are added to the stress index by extracting the relevant meteorological parameters from the ERA5 database (temperature and precipitation) to compute the drought indices (e.g. KBDI/SPI/SPIE) and water availability (AWC) with the dominant soil type, supplemented with supporting indices from the satellite data (e.g. NDWI/SAVI/EVI-2).

For reforestation projects, it is vital to monitor the impact of environmental parameters on plant health and stress, and to assist with the forest maintenance of the sites, we built time series models for temperature, precipitation, and various vegetation indices to create a baseline for site-specific growing conditions. Deep Learning (DL) models like semantic segmentation based on Convolutional Neural Network (CNN) can be built on top of it using transfer learning to extract the features from pre-trained models using large (global) datasets. The model can not only predict tree health but can also be used to predict growing conditions in the near future by flagging out potential dry periods before they happen.

The high-resolution remote sensed products are available in the (sub)tropical zone [30N - 30S], while the lower resolution products and the ERA5 data have a global cover. The test sites in this study are example sites, but the developed method can be applied to any reforestation monitoring project. The result of the analysis is a near-term growth indicator, which can be used to adjust the growing conditions of the site, as well as assist with the site selection for new reforestation projects (based on the established baseline and predictions).

The next step, after validation, is to create a dashboard where the user can select any location (within the data domain) and construct the baseline and prediction, based on available information.

How to cite: Van den Dool, H. G. J. and Bidwai, D.: Satellite data as a predictor for monitoring tree health and stress in reforestation projects, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13481, https://doi.org/10.5194/egusphere-egu23-13481, 2023.

How to cite: Pardo Meza, R., Quiané-Ruiz, J.-A., Demir, B., and Markl, V.: Wayang AgoraEO Plugin: The Framework for Scalable EO Workflows, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17046, https://doi.org/10.5194/egusphere-egu23-17046, 2023.