This dataset was created from all Landsat-8 images from South America in the year 2018. More than 31 thousand images were processed (15 TB of data), and approximately on half of them active fire pixels were found. The Landsat-8 sensor has 30 meters of spatial resolution (1 panchromatic band of 15m), 16 bits of radiometric resolution and 16 days of temporal resolution (revisit). The images in our dataset are in TIFF (geotiff) format with 10 bands (excluding the 15m panchromatic band).

Instructions: 

The images in our dataset are in georeferenced TIFF (geotiff) format with 10 bands. We cropped the original Landsat-8 scenes (with ~7,600 x 7,600 pixels) into image patches with 128 x 128 pixels by using a stride overlap of 64 pixels (vertical and horizontal). The masks are in binary format where True (1) represents fire and False (0) represents background and they were generated from the conditions set by Schroeder et al. (2016). We used the Schroeder conditions to process each patch, producing over 1 million patches with at least one fire pixel and the same amount of patches with no fire pixels, randomly selected from the original images.

The dataset is organized as follow. 

It is divided into South American regions for easy downloading. For each region of South America we have a zip file for images of active fire, its masks, and non-fire images. For example:

 - Uruguay-fire.zip

 - Uruguay-mask.zip

 - Uruguay-nonfire.zip

Within each South American region zip files there are the corresponding zip files to each Landsat-8 WRS (Worldwide Reference System). For example:

- Uruguay-fire.zip;

      - 222083.zip

      - 222084.zip

      - 223082.zip

      - 223083.zip

      - 223084.zip

      - 224082.zip

      - 224083.zip

      - 224084.zip

      - 225081.zip

      - 225082.zip

      - 225083.zip

      - 225084.zip

Within each of these Landsat-8 WRS zip files there are all the corresponding 128x128 image patches for the year 2018. 

 

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The raw data are collected from the websites of EPD (Environmental Protection Department, Hong Kong) and HKO (Hong Kong Observatory). Marine water quality data is provided by EPD and climatological data is provided by HKO. The data is interpolated by SAS “proc expand” and aligned to the beginning of each month.

 

The raw data used to produce this dataset are extracted from the following URL.

Instructions: 

The marine water samples are taken from 76 stations which are located in 10 water control zones. The water quality can be measured in 3 different water depths, namely ‘Surface’, Middle’ and Bottom’.

The columns of the water quality data are named in the format of “Zone + Station + Water depth +Water quality Parameter”. For example, the column “Zone1_TM2_Bot_VSSolids” contains the Volatile Suspended Solids data (mg/L) of the bottom-level water at the TM2 station, which is located in Zone 1.

 

The list of water quality parameters and Climatological parameters are tabulated below.

 

Water quality parameters

-------------------------------------

5-day Biochemical Oxygen Demand (mg/L)

Silica (mg/L)

Chlorophyll-a μg/L

pH

Temperature (C)

E. coli (cfu/100mL)

Faecal Coliforms (cfu/100mL)

Total Phosphorus (mg/L)

Orthophosphate Phosphorus (mg/L)

Volatile Suspended Solids (mg/L)

Phaeo-pigments (μg/L)

Suspended Solids (mg/L)

Dissolved Oxygen Saturation (%)

Dissolved Oxygen (mg/L)

Salinity (psu)

Secchi Disc Depth (M)

Nitrite Nitrogen (mg/L)

Nitrate Nitrogen (mg/L)

Ammonia Nitrogen (mg/L)

Total Nitrogen (mg/L)

Total Kjeldahl Nitrogen (mg/L)

Total Inorganic Nitrogen (mg/L)

Turbidity (NTU)

Unionized Ammonia (mg/L)

 

 

Climatological Parameters

--------------------

Mean Temp

Max Temp

Min Temp

Pressure

Dew Point Temp

Wet Bulb Temp

Mean Relative Humidity

Mean Amount Cloud

Rainfall

Grass Minimun Temperature

Bright Sunshine

Global Solar Radiation

Evaporation

Prevailing Wind Direction

Wind Speed

Sea Temperature

Reduced Visibility

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The large variability of system and types of heating load is a feature of the commercial metering of thermal energy. Heating consumption depends on many factors, for example, wall and roof material, floors number, system (opened and closed) etc. The daily data from heating meters in the residential buildings are presented in this dataset for comparing the thermal characteristics. These data are supplemented by floors number, wall material and year of construction, as well as data on average daily outdoor temperatures.

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 This data package is parepared by Dr. Jianguo Niu (IMSG at NOAA NESDIS/STAR) on

        March 18, 2020

 

 The purpose of this OMPS LFSO2 retrieval products package is in support the paper:

 "Evaluation and Improvement of the Near-real-time Linear Fit SO2 retrievals from Suomi NPP (S-NPP) Ozone Mapping & Profiler Suite"

       

This package includes LFSO2 V8TOS retrievals of:

        1. "logic swith on" (original set as described by th paper 01824) products

Instructions: 

 This data are in NetCDF format. Which can be read by an IDL code "rd_v8tos_nc.pro". The usage example

 

IDL>rd_v8tos_nc,filename,data

 

The "data" is a structure, which included most of the parameters you needed. 

 

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The dataset is mainly used for leak detection and localization.

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The target scene consists of a black card with six cocoa beans of three different fermentation levels (High, correct, and low fermentation), two beans for each class, whose false-color composite is shown in the provided Figure (a), ground-truth map is shown in Fig. (b), and Fig. (c) presents its representative spectral signatures. The spectral image was acquired by the AVT Stingray F-080B camera by acquiring one band each time from  350 - 950 nm. The acquired image has a spatial resolution of 1096x712 pixels and 300 spectral bands of 2 nm width.

 

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This is an observation data for water quality monitoring. 

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Attempts to prevent invasion of marine biofouling on marine vessels are demanding. By developing a system to detect marine fouling on vessels in an early stage of fouling is a viable solution. However, there is a  lack of database for fouling images for performing image processing and machine learning algorithm.

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As one of the research directions at OLIVES Lab @ Georgia Tech, we focus on the robustness of data-driven algorithms under diverse challenging conditions where trained models can possibly be depolyed. To achieve this goal, we introduced a large-sacle (~1.72M frames) traffic sign detection video dataset (CURE-TSD) which is among the most comprehensive datasets with controlled synthetic challenging conditions. The video sequences in the 

Instructions: 

The name format of the video files are as follows: “sequenceType_sequenceNumber_challengeSourceType_challengeType_challengeLevel.mp4”

·         sequenceType: 01 – Real data 02 – Unreal data

·         sequenceNumber: A number in between [01 – 49]

·         challengeSourceType: 00 – No challenge source (which means no challenge) 01 – After affect

·         challengeType: 00 – No challenge 01 – Decolorization 02 – Lens blur 03 – Codec error 04 – Darkening 05 – Dirty lens 06 – Exposure 07 – Gaussian blur 08 – Noise 09 – Rain 10 – Shadow 11 – Snow 12 – Haze

·         challengeLevel: A number in between [01-05] where 01 is the least severe and 05 is the most severe challenge.

Test Sequences

We split the video sequences into 70% training set and 30% test set. The sequence numbers corresponding to test set are given below:

[01_04_x_x_x, 01_05_x_x_x, 01_06_x_x_x, 01_07_x_x_x, 01_08_x_x_x, 01_18_x_x_x, 01_19_x_x_x, 01_21_x_x_x, 01_24_x_x_x, 01_26_x_x_x, 01_31_x_x_x, 01_38_x_x_x, 01_39_x_x_x, 01_41_x_x_x, 01_47_x_x_x, 02_02_x_x_x, 02_04_x_x_x, 02_06_x_x_x, 02_09_x_x_x, 02_12_x_x_x, 02_13_x_x_x, 02_16_x_x_x, 02_17_x_x_x, 02_18_x_x_x, 02_20_x_x_x, 02_22_x_x_x, 02_28_x_x_x, 02_31_x_x_x, 02_32_x_x_x, 02_36_x_x_x]

The videos with all other sequence numbers are in the training set. Note that “x” above refers to the variations listed earlier.

The name format of the annotation files are as follows: “sequenceType_sequenceNumber.txt“

Challenge source type, challenge type, and challenge level do not affect the annotations. Therefore, the video sequences that start with the same sequence type and the sequence number have the same annotations.

·         sequenceType: 01 – Real data 02 – Unreal data

·         sequenceNumber: A number in between [01 – 49]

The format of each line in the annotation file (txt) should be: “frameNumber_signType_llx_lly_lrx_lry_ulx_uly_urx_ury”. You can see a visual coordinate system example in our GitHub page.

·         frameNumber: A number in between [001-300]

·         signType: 01 – speed_limit 02 – goods_vehicles 03 – no_overtaking 04 – no_stopping 05 – no_parking 06 – stop 07 – bicycle 08 – hump 09 – no_left 10 – no_right 11 – priority_to 12 – no_entry 13 – yield 14 – parking

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As one of the research directions at OLIVES Lab @ Georgia Tech, we focus on the robustness of data-driven algorithms under diverse challenging conditions where trained models can possibly be depolyed.

Instructions: 

The name format of the provided images are as follows: "sequenceType_signType_challengeType_challengeLevel_Index.bmp"

  • sequenceType: 01 - Real data 02 - Unreal data

  • signType: 01 - speed_limit 02 - goods_vehicles 03 - no_overtaking 04 - no_stopping 05 - no_parking 06 - stop 07 - bicycle 08 - hump 09 - no_left 10 - no_right 11 - priority_to 12 - no_entry 13 - yield 14 - parking

  • challengeType: 00 - No challenge 01 - Decolorization 02 - Lens blur 03 - Codec error 04 - Darkening 05 - Dirty lens 06 - Exposure 07 - Gaussian blur 08 - Noise 09 - Rain 10 - Shadow 11 - Snow 12 - Haze

  • challengeLevel: A number in between [01-05] where 01 is the least severe and 05 is the most severe challenge.

  • Index: A number shows different instances of traffic signs in the same conditions.

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