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Satellite images can be a powerful source of data for analyses of conflict dynamics and social movements, but sociology has been slow to develop methods and metadata standards for transforming those images into data. We ask: How can satellite images become useful data? What are the key methodological and ethical considerations for incorporating high-resolution satellite images into conflict research? Why are metadata important in this work? We begin with a review of recent developments in satellite-based social scientific work on conflict, then discuss the technical and epistemological issues raised by machine processing of satellite information into user-ready images. We argue that high-resolution images can be useful analytical tools provided they are used with full awareness of their ethical and technical parameters. To support our analysis, we draw on two novel studies of satellite data research practices during the Syrian war. We conclude with a discussion of specific methodological procedures tried and tested in our ongoing work.

Satellite-based weather index insurance has recently been considered in order to reduce the high basis risk of station-based weather index insurance. However, the use of satellite data with a relatively low spatial resolution has not yet made it possible to determine the satellite indices free of disturbing landscape elements such as mountains, forests and lakes.

In this context, the Normalized Difference Vegetation Index (NDVI) was used based on both Moderate Resolution Imaging Spectroradiometer (MODIS) (250 × 250 m) and high-resolution Landsat 5/8 (30 × 30 m) images to investigate the effect of a higher spatial resolution of satellite-based weather index contracts for hedging winter wheat yields. For three farms in north-east Germany, insurance contracts both at field and farm level were designed.

The results indicate that with an increasing spatial resolution of satellite data, the basis risk of satellite-based weather index insurance contracts can be reduced. However, the results also show that the design of NDVI-based insurance contracts at farm level also reduces the basis risk compared to field level. The study shows that higher-resolution satellite data are advantageous, whereas satellite indices at field level do not reduce the basis risk.

To the best of the author’s knowledge, the effect of increasing spatial resolution of satellite images for satellite-based weather index insurance is investigated for the first time at the field level compared to the farm level.

The purpose of this paper is to ensure the stable operation of satellites in orbit and to assist ground personnel in continuously monitoring the satellite telemetry data and finding anomalies in advance, which can improve the reliability of satellite operation and prevent catastrophic losses.

This paper proposes a deep auto-encoder (DAE) satellite anomaly advance warning framework for satellite telemetry data. Firstly, this study performs grey correlation analysis, extracts important feature attributes to construct feature vectors and builds the variational auto-encoder with bidirectional long short-term memory generative adversarial network discriminator (VAE/BLGAN). Then, the Mahalanobis distance is used to measure the reconstruction score of input and output. According to the periodic characteristic of satellite operation, a dynamic threshold method based on periodic time window is proposed. Satellite health monitoring and advance warning are achieved using reconstruction scores and dynamic thresholds.

Experiment results indicate DAE methods can probe that satellite telemetry data appear abnormal, trigger a warning before the anomaly occurring and thus allow enough time for troubleshooting. This paper further verifies that the proposed VAE/BLGAN model has stronger data learning ability than other two auto-encoder models and is sensitive to satellite monitoring data.

This paper provides a DAE framework to apply in the field of satellite health monitoring and anomaly advance warning. To the best of the authors’ knowledge, this is the first paper to combine DAE methods with satellite anomaly detection, which can promote the application of artificial intelligence in spacecraft health monitoring.

The purpose of this study is to evaluate the potential of the DMC satellite data as suitable alternative to Landsat‐7 ETM+ satellite data sets in view of the aging conditions, the failure of the Scan Line Corrector (SLC) and resulting scan line anomaly within the Landsat‐7 ETM+ data sets.

The DMC and Landsat‐7 ETM+ satellite data were compared by obtaining quantitative parameters such as; spatial, geometric, spectral, radiometric properties from coincident date images of the two satellite sensors, while inter‐relationship between DMC and Landsat‐7 ETM+ satellite data were established by deriving sensor inter‐calibration from linear regression equation.

The result shows that the performances of UK‐DMC match well with Landsat‐7 ETM+ and the accuracy of the UK‐DMC with respect to spatial, geometric properties. The frequency of DN distribution in each waveband for the two sensors and a pair‐wise relationship between the DN of selected targets was established using linear regression equation with coefficient of determination (R²) values that range from 0.92 to 0.95. These are adequate for data integration of the UK‐DMC and Landsat‐7 ETM+ sensors, and indicate that UK‐DMC can be used as a replacement for Landsat‐7 ETM+ and can provide a suitable platform for multi‐temporal and multi‐sensor approach that is required for the study of environmental dynamics.

The challenge in this study is that information on DMC sensor calibration and radiometric parameters such as time‐dependent change in the data derived from pre‐flight measurements, in‐flight calibration and ground‐based calibration data were not available at the time of this study. Therefore, absolute radiometric correction of converting the digital number (DN) recorded by the sensor to spectral radiance detected by the sensor using sensor‐specific calibration parameters was not possible. The suitable alternative is to use spectrally invariant targets for relative radiometric correction of DN to DN pair wise pixel technique and selecting similar targets on the images.

This study shows that a suitable platform for multi‐temporal and multi‐sensor approach that is required for the study of environmental dynamics can be provided.

The issue of climate change was mentioned in this manuscript because satellite sensors that were used previously for climate change (multi‐disciplinary approach) does not have the temporal dynamics of daily coverage (temporal) and spatial resolution like the DMC satellites while there is usually a trade‐off between temporal and spatial resolution. The DMC satellites have got the exceptional capability of daily temporal and medium spatial resolution that can be suitable for monitoring climate change. That is why in this study investigation was carried out on the unique properties of the DMC satellites by making comparative assessments Landsat‐7 ETM+.

The originality/value of this paper lies on the fact that; for the first time the DMC satellite data are being compared with Landsat‐7 ETM+ because of similar characteristics in terms of wavebands (near infrared, red and green) and spatial resolution (Landsat‐7 ETM+: 30 m, DMC: 32 m).

For some time past satellites have been orbiting the Earth and sending back images of it to ground stations. These remote‐sensing images are now accumulating in large numbers: present plans for satellite launches indicate that the growth rate will continue to rise further into the 1990s. In consequence, satellite remote‐sensing data are raising urgent questions concerning their efficient storage and rapid retrieval. The experience of handling of remote‐sensing images is likely to influence future developments in computer graphics information more generally. We review here the current position regarding these problems and the steps being taken to overcome them.

The purpose of this study is to address a highly heterogeneous rift margin environment and exhibit considerable spatiotemporal hydro-climatic variations. In spite of limited, random and inaccurate data retrieved from rainfall gauging stations, the recent advancement of satellite rainfall estimate (SRE) has provided promising alternatives over such remote areas. The aim of this research is to take advantage of the technologies through performance evaluation of the SREs against ground-based-gauge rainfall data sets by incorporating its applicability in calibrating hydrological models.

Selected multi satellite-based rainfall estimates were primarily compared statistically with rain gauge observations using a point-to-pixel approach at different time scales (daily and seasonal). The continuous and categorical indices are used to evaluate the performance of SRE. The simple scaling time-variant bias correction method was further applied to remove the systematic error in satellite rainfall estimates before being used as input for a semi-distributed hydrologic engineering center's hydraulic modeling system (HEC-HMS). Runoff calibration and validation were conducted for consecutive periods ranging from 1999–2010 to 2011–2015, respectively.

The spatial patterns retrieved from climate hazards group infrared precipitation with stations (CHIRPS), multi-source weighted-ensemble precipitation (MSWEP) and tropical rainfall measuring mission (TRMM) rainfall estimates are more or less comparably underestimate the ground-based gauge observation at daily and seasonal scales. In comparison to the others, MSWEP has the best probability of detection followed by TRMM at all observation stations whereas CHIRPS performs the least in the study area. Accordingly, the relative calibration performance of the hydrological model (HEC-HMS) using ground-based gauge observation (Nash and Sutcliffe efficiency criteria [NSE] = 0.71; R² = 0.72) is better as compared to MSWEP (NSE = 0.69; R² = 0.7), TRMM (NSE = 0.67, R² = 0.68) and CHIRPS (NSE = 0.58 and R² = 0.62).

Calibration of hydrological model using the satellite rainfall estimate products have promising results. The results also suggest that products can be a potential alternative source of data sparse complex rift margin having heterogeneous characteristics for various water resource related applications in the study area.

This research is an original work that focuses on all three satellite rainfall estimates forced simulations displaying substantially improved performance after bias correction and recalibration.

Discusses an attempt by the British Library Document Supply Centre to exploit the capabilities of electronic mail linked to satellite transmission as a channel for large scale document delivery. Examines the background, transmission procedure, mode of operation, and future directions for electronic document delivery. Surmises that satellite transmission′s most serious competition comes from ISDN, although the likelihood is that the two will merge to carry a multitude of services.

The purpose of this paper is to show that satellite data applicability for landslides studies is given concentration in tropical regions, which have two limitations; regular cloud cover and thick vegetation.

Landslide studies have three categories: mapping, zonation, and monitoring. High spatial resolution images are convenient for mapping. Since the slope and slope materials are the dominant parameters for slide potential, a high resolution DEM produced from the above data with classification of multispectral data will be vital for zonation. Weather‐free and penetration are advantages that make radar images essential for monitoring.

A composition of satellite data with support of aerial photography, with its high spatial resolution, will give an excellent spatial database for these studies.

Satellite remote sensing data are applicable for landslides studies in non‐accessible mountainous tropical regions.

This study aims to propose a new method to predict retail store performance using publicly available satellite imagery data and machine learning (ML) algorithms. The goal is to provide manufacturers and other practitioners with a more accurate and objective way to assess potential channel members and mitigate information asymmetry in channel selection and negotiation.

The authors developed an open-source approach using publicly available Google satellite imagery and ML algorithms. A computer vision algorithm was used to count cars in store parking lots, and the data were processed with a CNN. Linear regression and various ML algorithms were used to estimate the relationship between parked cars and sales.

The relationship between parked cars and sales was nonlinear and dependent on the type of channel member. The best model, a Stacked Ensemble, showed that parking lot occupancy could accurately predict channel member performance.

The proposed approach offers manufacturers a low-cost and scalable solution to improve their channel member selection and performance assessment process. Using satellite imagery data can help balance the marketing channel planning process by reducing information asymmetry and providing a more objective way to assess potential partners.

This research is unique in proposing a method based on publicly available satellite imagery data to assess and predict channel member performance instead of forward-looking sales at the firm and industry levels like previous studies.