Select 'Logging'> 'Start' from the ribbon to start logging.Set the 'Target' to 'MQTT' if using the SparkplugB option, or 'MQTT_JSON_KepwareIOT' if using the JSON option.By default, it is configured to publish data to a local broker using the anonymous port, 1883. This example shows the SparkplugB option.There are two different xml files that can edited depending if you want to send data using a SparkplugB payload ( SAF_) or a JSON payload ( SAF_DataGenerationTarget.MQTT_JSON_KepwareIOT.xml).This will generate sample files in C:\ProgramData\Canary\Data Generation which will then need edited.
Select the 'Create Sample Target Files' from the 'Testing' drop-down list in the ribbon.This tool can be configured to write data directly to the historian or to an MQTT broker by changing the 'Target' parameter. The Data Generation tool can be run by opening C:\Program Files\Canary\Collectors\Data Generator\CanaryDataGenerator.exe. Likewise, many future methodologies will certainly rely on the combination of Tuplekeys and Datacubes concepts which have been tested within the MMT tool.The Data Generation tool can be installed by selecting it from the Data Collectors list on the Canary Installer. The information obtained from this work will allow the estimation of the water requirements of citrus orchards and the improvement of irrigation scheduling methodologies. Additionally, our results highlight the importance of optical data to provide acceptable classification performance especially for a complex landscape such as that of the Cap Bon. Our results demonstrate the usefulness of the new data management tool in organizing the input classification data. As for the scenario relying on the combination of optical and SAR data (NDVI + VV + VH), it presented an OA = 0.58. This result was obtained by support vector machine (SVM). The obtained results revealed that the best classification performance and highest accuracy were obtained with the scenario using only optical-based information (NDVI), with an overall accuracy OA = 0.76. Two different classification processes were performed based on the selection of the input feature (the obtained time-series as input data: NDVI and NDVI + VV + VH) and on the most accurate algorithm for each scenario (22 tested classifiers). The Datacubes concept allows to provide an arranged array of time-series multi-dimensional stacks (space, time and data) of gridded data. Tuplekeys or special spatial regions were created within a LNG to allow a proper integration between the data of both sensors. This tool is based on the combination of two concepts: (i) the local nested grid (LNG) called Tuplekeys and (ii) Datacubes.
This plugin was designed to manage large Earth observation (EO) data.
Thus, we described the new QGIS plugin “Model Management Tool (MMT)”. This classification was carried out in the Cap Bon region, Tunisia, to classify citrus groves among two other crop classes (olive groves and open field) using multi-temporal remote sensing data from Sentinel- 1 and Sentinel-2 satellite platforms. This paper proposes an innovative approach of remote sensing data management that was used to prepare the input data for the crop classification application. Nevertheless, the performance of crop classification using large spatio-temporal data remains challenging due to the difficulties in handling huge amounts of input data (different spatial and temporal resolutions). High-resolution and regional-scale maps of agricultural land are required to develop better adapted future strategies. Remote sensing products provide essential information for the crop classification application, which is used to produce thematic maps. In the context of a changing climate, monitoring agricultural systems is becoming increasingly important.
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