This dataset contains actual field/experimental data for the following environmental engineering applications, namely:

  • Concentration data generated from filtration systems which treat influents, having contaminant materials, via adsorption process.
  • Streamflow height data collated for 50 states/cities in America for the historical period between 1900-2018.
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This dataset contains the collection of temperature, humidity, heat index, thermal discomfort index and temperature and humidity index of five indoor environments in São Paulo, Brazil. Data were collected using the ESP32 microcontroller and the DHT11 temperature and humidity sensor.

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This dataset was used to quantify the effects of environmental change on SSTDR measurements from solar panels. We collect illuminance (Lux), temperature (deg F), and humidity (%) alongside SSTDR waveforms on a fault free string. Data is collected once per minute in January 2020, and twice per minute in August-September 2020. 

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More than 40% of energy resources are consumed in the residential buildings, and most of the energy is used for heating. Improving the energy efficiency of residential buildings is an urgent problem. The collected data is intended to study a dependence of the dynamics heat energy supply from outside temperature and houses characteristics, such as walls material, year of construction, floors amount, etc. This study will support the development of methods for comparing thermal characteristics of residential buildings and carry out recommendations for the energy efficiency increases.

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More than 40% of energy resources are consumed in the residential buildings, and most of the energy is used for heating. Improving the energy efficiency of residential buildings is an urgent problem. The collected data is intended to study a dependence of the dynamics heat energy supply from outside temperature and houses characteristics, such as walls material, year of construction, floors amount, etc. This study will support the development of methods for comparing thermal characteristics of residential buildings and carry out recommendations for the energy efficiency increases.

Instructions: 

Dataset "teplo.csv" is a simple text file. Each heating meter forms one daily record. The dataset has been collected during eight heating seasons in houses of Tomsk (Russia).

All table rows are the following.

Date - date in Windows format.
M1 - the mass of the input water (heat carrier) per day.
M2 - the mass of the output water. If the residential building has an open heating system (hot water flows from the heating system), M2 is less than M1.
Delta_M = difference M2-M1. It is the technological parameter that allows the equipment observation for buildings with the closed system.
T1 - the average temperature of the heating carrier in the input of the heating system. It is the independent variable from home characteristics.
T2 - the average temperature of the heating carrier in the output. It is the dependent variable both from T1 and heating consumption at the building.
Delta_T = difference T2-T1.
Q =M1*(T2-T1) - amount of the consumed heating in Gcal.
USPD - ID of the heating meter. Some residential buildings have not the only one heating meters.
YYYYMM - date in the format year-month YYYYMM.
Registrated - heating or heating plus hot water that under registration.
Scheme - the type of the heating system (opened or closed).
Type - code system-load (4 digits). First digit 1 is opened system, 2 is a closed system. The second digit 0 is heating, 1 is heating and hot water supply. The third and fourth digits are floor amount (01, 02, 03, ..., 17).
Area - the area of building that heating meter is served.
Floors - the amount of building floors.
Walls_material - walls material.
Year_of_construction - the year of building construction.
Area_of_building - total area of the building.
Temperature - outdoor temperature by RosHydromet website.
Inhabitants - the amount of inhabitants in the house.

The Python program "viborka_house.zip" allows you to select from the file "teplo.csv" rows that belongs to the same heating meter USPD. This allows receiving of heat consumption series from a particular house and the outside air temperature in this day. After "viborka.py" starting the user enters the USPD number, names of the input, and output files.

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Segmentation of TC clouds in 2016. The segmentation task was accomplished by an algorithm which takes a time series of brightness temperature images of TCs and uses image processing techniques to acquire segmentation for each image in a semi-supervised manner. 

Instructions: 

2016 TC cloud segmentation animation

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Here we introduce so-far the largest subject-rated database of its kind, namely, "Effect of Paddy Rice vegetation on received signal strength between CC2538 SoC 32-bit Arm Cortex-M3 based sensor nodes operating at 2.4 GHz Radio Frequency (RF)". This database contains received signal strength measurements collected through campaigns in the IEEE 802.15.4 standard precision agricultural monitoring infrastructure developed for Paddy Rice crop monitoring from the period 01/07/2020 to 03/11/2020.

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Here we introduce so-far the largest subject-rated database of its kind, namely, "Effect of Millet vegetation on path-loss between CC2538 SoC 32-bit Arm Cortex-M3 based sensor nodes operating at 2.4 GHz Radio Frequency (RF)". This database contains received signal strength measurements collected through campaigns in the IEEE 802.15.4 standard precision agricultural monitoring infrastructure developed for millet crop monitoring from period 03/06/2020 to 04/10/2020.

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The MATLAB data file “wind_speed_2015_2016_10minutely.mat” was obtained based on the original wind speed data downloaded from "Iowa Environmental Mesonet: AWOS Network Database" at the localities of Le Mars, Orange City, and Sheldon in Iowa, USA, recorded from 2015 to 2016. The raw data set has varying resolution, ranging from 5 to 10 min per sample. A fraction of missing measurements were filled in by interpolation. The resulting data-set was then re-sampled at a fixed rate of 10 min per sample, resulting in 105120 data points for each location.

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These last decades, Earth Observation brought quantities of new perspectives from geosciences to human activity monitoring. As more data became available, artificial intelligence techniques led to very successful results for understanding remote sensing data. Moreover, various acquisition techniques such as Synthetic Aperture Radar (SAR) can also be used for problems that could not be tackled only through optical images. This is the case for weather-related disasters such as floods or hurricanes, which are generally associated with large clouds cover.

Instructions: 

The dataset is composed of 336 sequences corresponding to areas in West and South-East Africa, Middle-East, and Australia. Each time series is located in a given folder named with the sequence ID (0001... 0336).

Two json files, S1list.json and S2list.json are provided to describe respectively the Sentinel-1 and Sentinel-2 images.The keys are the total number of images in the sequence, the folder name, the geography of the observed area, and the description of each image in the series. The SAR images description contains also the URLs to download the images.Each image is described by its acquisition date, its label (FLOODING: boolean), a boolean (FULL-DATA-COVERAGE: boolean) indicating if the area is fully or partially imaged, and the file prefix. For SAR images the orbit (ASCENDING or DESCENDING) is also indicated.

The Sentinel-2 images were obtained from the Mediaeval 2019 Multimedia Satellite Task [1] and are provided with Level 2A atmospheric correction. For one acquisition, there are 12 single-channel raster images provided corresponding to the different spectral bands.

The Sentinel-1 images were added to the dataset. The images are provided with radiometric calibration and range doppler terrain correction based on the SRTM digital elevation model. For one acquisition, two raster images are available corresponding to the polarimetry channels VV and VH.

The original dataset was split into 269 sequences for the train and 68 sequences for the test. Here all sequences are in the same folder.

 

To use this dataset please cite the following papers:

Flood Detection in Time Series of Optical and SAR Images, C. Rambour,N. Audebert,E. Koeniguer,B. Le Saux,  and M. Datcu, ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020, 1343--1346

The Multimedia Satellite Task at MediaEval2019, Bischke, B., Helber, P., Schulze, C., Srinivasan, V., Dengel, A.,Borth, D., 2019, In Proc. of the MediaEval 2019 Workshop

 

This dataset contains modified Copernicus Sentinel data [2018-2019], processed by ESA.

[1] The Multimedia Satellite Task at MediaEval2019, Bischke, B., Helber, P., Schulze, C., Srinivasan, V., Dengel, A.,Borth, D., 2019, In Proc. of the MediaEval 2019 Workshop

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