This chipped training dataset is over Shanghai and includes 30cm high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 or smaller pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in developing the ramp baseline model and contains 3,574 tiles and 7,118 buildings. The original dataset was sourced from the SpaceNet 2 Dataset before the imagery was tiled down from 650x650 pixel chips and labels were revised to be consistent with the ramp datasets notion of rooftop as the building footprint. Dataset keywords: Urban, Dense.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Accurate monitoring of air quality can reduce its adverse impact on earth. Ground-level sensors can provide fine particulate matter (PM2.5) concentrations and ground images. But, such sensors have limited spatial coverage and require deployment cost. PM2.5 can be estimated from satellite-retrieved Aerosol Optical Depth (AOD) too. However, AOD is subject to uncertainties associated with its retrieval algorithms and constrain the spatial resolution of estimated PM2.5. AOD is not retrievable under cloudy weather as well. In contrast, satellite images provide continuous spatial coverage with no separate deployment cost. Accuracy of monitoring from such satellite images is hindered due to uncertainties of sensor data of relevant environmental parameters, such as, relative humidity, temperature, wind speed and wind direction . Belief Rule Based Expert System (BRBES) is an efficient algorithm to address these uncertainties. Convolutional Neural Network (CNN) is suitable for image analytics. Hence, we propose a novel model by integrating CNN with BRBES to monitor air quality from satellite images with improved accuracy. We customized CNN and optimized BRBES to increase monitoring accuracy further. An obscure image has been differentiated between polluted air and cloud based on relationship of PM2.5 with relative humidity. Valid environmental data (temperature, wind speed and wind direction) have been adopted to further strengthen the monitoring performance of our proposed model. Three-year observation data (satellite images and environmental parameters) from 2014 to 2016 of Shanghai have been employed to analyze and design our proposed model.

Source code and dataset

We implement our proposed integrated algorithm with Python 3 and C++ programming language. We process the satellite images with OpenCV library. Keras library functions are used to implement our customized VGG Net. We write python script smallervggnet.py to build this VGG Net. Next, we train and test this network with a dataset of satellite images through train.py script. This dataset consists of 3-year satellite images of Oriental Pearl Tower, Shanghai, China from Planet from January-2014 till December-2016 (Planet Team, 2017). These images are captured by PlanetScope, which is a constellation composed by approximately 120 optical satellites operated by Planet (Planet Team, San Francisco, CA, USA, 2016). Based on the level of PM2.5, this dataset is divided into three classes: HighPM, MediumPM and LowPM. We classify a new satellite image (201612230949.png) with our trained VGG Net by classify.py script. Standard file I/O is used to feed this classification output to the first BRBES (cnn_brb_1.cpp) through a text file (cnn_prediction.txt). In addition to VGG Net classification output, cloud percentage and relative humidity are fed as input to first BRBES. We write cnn_brb_2.cpp to implement second BRBES, which takes the output of first BRBES, temperature and wind speed as its input. Wind direction based recalculation of the output of second BRBES is also performed in this cpp file to compute the final monitoring value of PM2.5. We demonstrate this code architecture through a flow chart in Figure 5 of the manuscript.Source code and dataset of the satellite images are made freely available through the published compute capsule (https://doi.org/10.24433/CO.8230207.v1).

Code: MIT license; Data: No Rights Reserved (CC0)

The dataset was originally published in DiVA and moved to SND in 2024.

Abstract

Regional transport and vertical mixing are important for aerosol pollution of megacities, but their roles are often challenging to assess via ground observations. In this study, we measured aerosol chemical composition simultaneously above urban canopy (Shanghai Tower, 609 m), a site representative of aerosols from regional scale, and a nearby ground site and investigated the roles of regional transport and vertical mixing over 2020 COVID-19 lockdown period (Jan.1-Apr. 18), when local emissions were first drastically reduced and then recovered. During the lockdown period, regional transport was the major source of most aerosol species (organics, NO₃^-, SO₄^2-, and NH₄⁺) at both heights according to the high correlation coefficients (R=0.81~0.87) between both heights. Correlation coefficients and vertical ratios (609 m/ground) of most aerosol components showed similar diurnal variations with the evolution of planetary boundary layer height, indicating the role of vertical mixing in aerosol pollution. Moreover, the concentrations of aerosol components at 609 m generally preceded those at ground level by 1~2 h, indicating that aerosols were first transported at upper boundary layer, and then were mixed downwards to ground level. At 609 m, highly oxidized oxygenated organic aerosol (OOA; a surrogate of secondary organic aerosol (SOA)) dominated in organic aerosol (≥75%). The high correlations (R=0.96) between OOA and hydrocarbon-like organic aerosol (HOA; a surrogate of primary organic aerosol (POA)) at 609 m indicated that they originated similarly from regional transport. This study highlights the importance of regional joint prevention and control of pollutant emissions and observation above urban canopy.

The data files are provided for readers to evaluate this research.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Automatic, accurate crop type maps can provide unprecedented information for understanding food systems, especially in developing countries where ground surveys are infrequent. However, little work has applied existing methods to these data scarce environments, which also have unique challenges of irregularly shaped fields, frequent cloud coverage, small plots, and a severe lack of training data. To address this gap in the literature, we provide the first crop type semantic segmentation dataset of small holder farms, specifically in Ghana and South Sudan. We are also the first to utilize high resolution, high frequency satellite data in segmenting small holder farms.

The dataset includes time series of satellite imagery from Sentinel-1, Sentinel-2, and PlanetScope satellites throughout 2016 and 2017. For each tile/chip in the dataset, there are time series of imagery from each of the satellites, as well as a corresponding label that defines the crop type at each pixel. The label has only one value at each pixel location, and assumes that the crop type remains the same across the full time span of the satellite image time series. In many cases where ground truth was not available, pixels have no label and are set to a value of 0.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.