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.
Truth discovery techniques, which can obtain accurate aggregation results based on the weighted sensory data of users, are widely adopted in industrial sensing systems. However, there are some privacy matters that cannot be ignored in truth discovery process. While most of the existing privacy preserving truth discovery methods focus on the privacy of sensory data, they may neglect to protect the privacy of another equally important information, the tagged location information.
The project is conceptualized to 'Geo Web-Based Facility Mapping for Zone-2 in Greater Visakhapatnam Municipal Corporation- GVMC in Visakhapatnam, India'. The main objective is to share the spatial data to the public to help them find the information about the nearest Hospital, ATM, Educational institutions, petrol filling stations, and supermarkets by providing both web map services and web coverage services using QGIS Cloud.
Accurate information about crop rotation is essential for administrators, managers and various government departments for assessment, monitoring, and management of various resources for crop escalation. Radar remote sensing, because of its all-weather capability and assured uninterrupted data supply can show a substantial part in the evaluation of crop rotation.
Empirical line methods (ELM) are frequently used to correct images from aerial remote sensing. Remote sensing of aquatic environments captures only a small amount of energy because the water absorbs much of it. The small signal response of the water is proportionally smaller when compared to the other land surface targets.
This dataset presents some resources and results of a new approach to calibrate empirical lines combining reference calibration panels with water samples. We optimize the method using python algorithms until reaches the best result.
The files are identified sequentially according to the processing step:
- A1-img-nd_samples.xlsx: Digital numbers of water samples extract from the hyperspectral image
- A2-img-nd_targets.xlsx: Digital numbers of reference targets extract from the hyperspectral image
- B1-asd-rad_refl_targets.xlsx: Radiance values collected with ASD HandHeld of the reference targets and calculated Reflectance
- B2-asd-simulatedbands_refl.xlsx: Target reflectance values calculated and simulated to match the hyperspectral camera response function
- C1-trios-rad_refl_samples.xlsx: Radiance values collected with TriOS of the water points and calculated Reflectance
- C2-trios-simulatedbands_refl.xlsx: Water reflectance values calculated and simulated to match the hyperspectral camera response function
- D1-nd_data.csv: Digital number extracted from the hyperspectral image (CSV format, this is the input of the algorithm)
- D1-nd_data.xlsx: Digital number extracted from the hyperspectral image (xlsx format)
- D2-r_data.csv: Reflectance calculated from the spectroradiometers measurements (CSV format, this is the input of the algorithm)
- D2-r_data.xlsx: Reflectance calculated from the spectroradiometers measurements (xlsx format)
- D3-r_nd_targets.xlsx: Agregation from D1 and D2 data to compare the data
- E1-calc_coef_line.py: Python algorithm to calibrate and validate the empirical line model
- Fit.py: Python script class to calculate the Fit of linear and exponential function
- output_graphs.zip: The results of the graphs generated for each of the evaluated combinations. In this package are different graphical representations for each of the combinations of samples and targets, as well as for the exponential and linear fits.
All files of the output folder are self-explained, because the filename identifies how the ELM was calibrated.
Details and descriptions about the full process steps are in the official paper (under journal review).
Emergency managers of today grapple with post-hurricane damage assessment that is often labor-intensive, slow,costly, and error-prone. As an important first step towards addressing the challenge, this paper presents the development of benchmark datasets to enable the automatic detection ofdamaged buildings from post-hurricane remote sensing imagerytaken from both airborne and satellite sensors. Our work has two major contributions: (1) we propose a scalable framework to create benchmark datasets of hurricane-damaged buildings
Data can be used for object detection algorithms to properly annotate post disaster buildings as either damaged or non damaged aiding disaster response. This dataset contains ESRI Shapefiles of bounding boxes of buildings labeled as either non-damaged or damaged. Those labeled as damaged also have four degrees of damage from minor to catastrophic. Importantly, each bounding box is also indexed to one of the images in the NOAA post Harvey hurricane imagery dataset allowing users to match the bounding boxes with the correct imagery for training the algorithm.