Accurately mapping vegetation is crucial for environmental monitoring. Traditional methods for identifying shrubs are labor-intensive and impractical for large areas. This workflow uses remote sensing and deep learning to detect Juniperus shrubs from high-resolution RGB satellite images, making shrub identification more efficient and accessible to non-experts in machine learning. Background In a dynamic climate, accurately mapping vegetation distribution is essential for environmental monitoring, biodiversity conservation, forestry, and urban planning. One important application of vegetation mapping is the identification of shrub individuals. We term by shrub identification, detection of shrub location and segmentation of shrub morphology. Introduction Yet, shrub species monitoring is a challenging task. Ecologists used to identify shrubs using classical field surveying methods, however, this process poses a significant challenge since the shrubs are often distributed in large areas that are most of the time inaccessible. Thus, these methods are considered labor-intensive, costly, time-consuming, unsustainable, limited to a small spatial and temporal scale, and their data are often not publicly available. Combining remote sensing and deep learning, however, can play a significant role in tackling these challenges providing a great opportunity to improve plant surveying. First, remote sensing can offer highly detailed spatial resolution granting exceptional flexibility in data acquisition. Then, these data can be afterward processed by deep learning models for automatic identification of shrubs. Aims The objective of this workflow is to help scientists, non-expert in machine learning, detect Juniperus shrubs from RGB very-high resolution satellite images using deep learning and remote sensing tools. Scientific Questions Can we accurately detect high-mountain Juniperus shrubs from RGB very-high resolution satellite images using deep learning?

The objective of this service is to implement a test network on blockchain technology for the permanent storage of data related to ecosystem variables and datasets. This instance of the service has been designed as a test network and is supported under Hyperledger Besu technology. It has full operability for tokens and smart contracts, with support for IBTF consensus and provides a service for reading and writing hashes stored in IPFS systems. Technical data for this testnet: - Name of the net: LifeBlock Testnet - NetworkId: 3170 - Coin: LifeCoin - Native token: LifeToken - Coins issued: 9.200.000 - Gas price: 0

CakeShop is a set of tools and APIs for working with Hyperledger Besu/Quorum, packaged as a Java web application archive (WAR). Tools included are blockchain explorer, node info, peer management, echanced permissions UI, solidity sandbox and contract registry. CakeShop will allow a more faster and secure deployment of LifeBlock nodes and resources.

Kibana is a free and open frontend application that sits on top of the Elastic Stack and provides data visualization and search capabilities for data indexed in Elasticsearch. This service allows the connection of a monitorization instance of Kibana to LifeBlock in order to enable fast search.

This workflow employs a deep learning model for blind spectral unmixing, avoiding the need for expensive hyperspectral data. The model processes 224x224 pixel RGB images and associated environmental data to generate CSV files detailing LULC abundance at two levels of detail (N1 and N2). The aim is to provide an efficient tool for LULC monitoring, answering the question: Can LULC abundance be estimated from RGB images and environmental data? This framework supports environmental monitoring and land cover analysis. Background Land Use and Land Cover (LULC) represents earth surface biophysical properties of natural or human origin, such as forests, water bodies, agricultural fields, or urban areas. Often, different LULC types are mixed together in the same analyzed area. Nowadays, spectral imaging sensors allow us to capture these mixed LULC types (i.e., endmembers) together as different spectral data signals. LULC types identification within a spectral mixture (i.e., endmembers identification) and their quantitative abundance assessment (i.e., endmembers abundances estimation) play a key role in understanding earth surface transformations and climate change effects. These two tasks are carried out through spectral unmixing algorithms by which the measured spectrum of a mixed image is decomposed into a collection of constituents (i.e., spectra, or endmembers), and a set of fractions indicating their abundances. Introduction Early research on spectral unmixing dates back more than three decades. First attempts, referred to as linear unmixing, assumed that the spectral response recorded for an LULC mixture is simply an additive function of the spectral response of each class weighted by its proportional coverage. Notably, some authors used linear regression and similar linear mixture-based techniques in order to relate the spectral response to its class composition. Afterwards, other authors claimed the necessity of overcoming this assumption by proposing non-linear unmixing methods. However, non-linear methods require endmember spectra extraction for each LULC class, which has been found difficult in several works. Moreover, some studies indicated that it is unlikely that the spectra could be derived directly from the remotely sensed data since the majority of image pixels may be mixed. To overcome these limitations, several works introduced what is called blind spectral unmixing as an alternative method to avoid the need to derive any endmember spectra or making any prior assumption about their mixing nature. However, the majority of works that adopted blind spectral unmixing used deep learning-based models trained with expensive and hard-to-process hyperspectral or multispectral images. Therefore, many researchers during the last decade pointed out that more effort should be dedicated towards the usage of more affordable remote sensing data with few bands in spectral unmixing. They justified this need by two important factors: (1) In real situations, we might have access to images with only a few bands because of their availability, cost-effectiveness, and acquisition time-efficiency in comparison to imagery gathered with multi-band devices that require more processing effort and expenses; (2) In some cases, we do not really need a huge number of bands, as they can be used as a fundamental dataset from which we determine optimal wavebands for a particular application. In parallel, high-quality research in artificial intelligence application to remote sensing imagery, such as computer vision-based techniques and especially DL, is continuously achieving new breakthroughs that encourage researchers to entrust remote sensing imagery analysis tasks to these models and be confident about their performance. Aims The objective of this work is to present what is to our knowledge the first study that explores a multi-task deep learning approach for blind spectral unmixing using only 224x224 pixels RGB images derived from Sentinel-2 and enriched with their corresponding environmental ancillary data (topographic and climatic ancillary data) without the need to use any expensive and complex hyperspectral or multispectral data. The proposed deep learning model used in this study is trained in a multi-task learning approach (MTL) as it constitutes the most adequate machine learning method that aims to combine several information from different tasks to improve the performance of the model in each specific task, motivated by the idea that different tasks can share common feature representations. Thus, the provided model in this workflow was optimized for elaborating endmembers abundance estimation task that aims to quantify the spatial percentage covered by each LULC type within the analyzed RGB image, while being trained for other spectral unmixing related tasks that improves its accuracy in the main targeted task which is endmembers abundance estimation. The provided model here is able to give for each input (RGB image + ancillary data) the contained endmembers abundances values inside its area summarized in an output CSV file. The results can be computed for two different levels N1 and N2. These two levels reflect two land use/cover levels definitions in SIPNA land use/cover mapping campaign (Sistema de Información sobre el Patrimonio Natural de Andalucía) which aims to build an information system on the natural heritage of Andalusia in Spain (https://www.juntadeandalucia.es/medioambiente/portal/landing-page-%C3%ADndice/-/asset_publisher/zX2ouZa4r1Rf/content/sistema-de-informaci-c3-b3n-sobre-el-patrimonio-natural-de-andaluc-c3-ada-sipna-/20151). The first level "N1" contains four high-level LULC classes, whereas the second level "N2" contains ten finer level LULC classes. Thus, this model was mainly trained and validated on the region of Andalusia in Spain. Scientific Questions Through the development of this workflow, we aim at addressing the following main scientific question: - Can we estimate the abundance of each land use/land cover type inside an RGB satellite image using only the RGB image and the environmental ancillary data corresponding to the area covered by this image?

IPFS is a peer-to-peer (p2p) storage network. Content is accessible through peers located anywhere in the LifeWatch ERIC network, that might relay information, store it, or do both. IPFS knows how to find files by using its content address rather than its location. It will be used mainly by the blockchain services.

The objective of this service is to enable the interaction of LifeBlock Testnet with a proprietary IPFS network through the use of smart contracts. This functionality allows the storage of the information identification hash in IPFS in LifeBlock Testnet. Technical data for this service: - URL: http://135.125.10.249:5000/ - Contract address: 0x42699a7612a82f1d9c36148af9c77354759b210b

Prometheus is an open source monitoring solution used to get data, make queries, provide visualizations and set up alerts inside Lifeblock Testnet and IPFS environment.

The objective of this service is to provide LifeBlock's infrastructure with a connection point to hardware and software wallets, particularly Metamask. Through this service it is possible to connect to LifeBlock to use, manage, transfer, create tokens and LifeCoins. Technical data for this service: - Name of the net: LifeBlock Testnet - NetworkId: 3170 - RPC URL:http://135.125.10.249:8545- Currency Symbol: LifeCoin - Block explorer URL:http://135.125.10.249:25000/

This workflow integrates the MEDA Toolbox for Matlab and Octave, focusing on data simulation, Principal Component Analysis (PCA), and result visualization. Key steps include simulating multivariate data, applying PCA for data modeling, and creating interactive visualizations. The MEDA Toolbox combines traditional and advanced methods, such as ANOVA Simultaneous Component Analysis (ASCA). The aim is to integrate the MEDA Toolbox into LifeWatch, providing tools for enhanced data analysis and visualization in research. Background This workflow is a template for the integration of the Multivariate Exploratory Data Analysis Toolbox (MEDA Toolbox, https://github.com/codaslab/MEDA-Toolbox) in LifeWatch. The MEDA Toolbox for Matlab and Octave is a set of multivariate analysis tools for the exploration of data sets. There are several alternative tools in the market for that purpose, both commercial and free. The PLS_Toolbox from Eigenvector Inc. is a very nice example. The MEDA Toolbox is not intended to replace or compete with any of these toolkits. Rather, the MEDA Toolbox is a complementary tool that includes several contributions of the Computational Data Science Laboratory (CoDaS Lab) to the field of data analysis. Thus, traditional exploratory plots based on Principal Component Analysis (PCA) or Partial Least Squares (PLS), such as score, loading, and residual plots, are combined with new methods: MEDA, oMEDA, SVI plots, ADICOV, EKF & CKF cross-validation, CSP, GPCA, etc. A main tool in the MEDA Toolbox which has received a lot of attention lately is ANOVA Simultaneous Component Analysis (ASCA). The ASCA code in the MEDA Toolbox is one of the most advanced internationally. Introduction The workflow integrates three examples of functionality within the MEDA Toolbox. First, there is a data simulation step, in which a matrix of random data is simulated with a user-defined correlation level. The output is sent to a modeling step, in which Principal Component Analysis (PCA) is computed. The PCA model is then sent to a visualization module. Aims The main goal of this template is the integration of the MEDA Toolbox in LifeWatch, including data simulation, data modeling, and data visualization routines. Scientific Questions This workflow only exemplifies the integration of the MEDA Toolbox. No specific questions are addressed.