Remote Sensing Technology Market valued at $19.47 Billion in 2023, and is projected to $USD 50.96 Billion by 2032, at a CAGR of 5.29% from 2023 to 2032.

This pilot study will focus on Florida Bay, a region that suffered the loss of 40,000 ha of turtle grass in a die-off event that began in 1987, and a small, localized die-off in 1999. These events were well documented and provide a baseline for testing methods of monitoring grass beds remotely. Remote sensing data, including aerial photos and satellite imagery data, and data extracted from sediment cores will be used to examine the long-term sequences of events leading up to seagrass die-off events. The objectives of this pilot study are to develop a methodology for monitoring spatial and temporal changes in sub-aquatic vegetation using remote sensing, satellite imagery, and aerial photography, and to analyze potential causes of seagrass die-off using geographic, geologic and biologic tools. The ultimate goal is to develop a method for forecasting potential sea-grass die-offs and to determine if remediation efforts would be cost-effective. Florida Bay is selected for the pilot study because the thorough documentation of the 1987-1988 die-off event provides a baseline for examining data preceding and succeeding the event. In addition, a small well studied die-off occurred in 1999-2000 at Barnes Key in Florida Bay. A 10-15 km2 portion of Florida Bay that encompasses areas affected by the 1987 and 1999 die-offs will be analyzed for this pilot study. Current remotely sensed data, aerial photos and satellite images from this area will be used to test different platforms, determine detection limits, and to attempt to isolate distinct signals for different types of vegetation. When ground-truthing is completed, archived remotely sensed data and/or aerial photographs can then be used to examine the sequences of events leading up to the die-offs. The remotely sensed data can be compared and compiled with the data collected by seagrass biologists in 1987 and 1999, and to sediment core data collected at the sites of seagrass die-off. Sediment cores provide a long-term perspective on changes in nutrient geochemistry, substrate, water chemistry (salinity, temperature, oxygen), and changes in the biota. The geologic, biologic and remotely sensed data will be integrated and analyzed to determine the patterns of change and sequences of events that occur in healthy seagrass beds and in beds undergoing a die-off. Several remote sensor types will be compared in this study to determine the ideal sensor bands and spatial resolution necessary to detect and monitor the health of seagrass beds. The sensors to be tested include Landsat 7 (30m multi-spectral spatial resolution), ASTER (15 and 30m multi-spectral), Quickbird (2.5m multi-spectral and <1m panchromatic), and large-scale aerial photography (anticipated spatial resolution .25m with visible and near-infrared bands). Imagery with bands in the blue wavelength may help to penetrate water and infrared or near-infrared bands are predicted to perform better for resolving vegetation. It is theorized that through a combination of blue, and infrared bands and higher spatial resolution it will be possible to map the extent of seagrass beds. Although Landsat ETM+ 7 has several bands in desirable wavelengths, this sensor is predicted to be too course of a dataset to resolve individual seagrass beds. Landsat ETM+ may be used to develop an index of chlorophyll values that may be translated into a measure of seagrass health. ASTER s multiple infrared bands and increased spatial resolution may be successful in distinguishing between the types of vegetation, but these bands are not designed for water penetration. Higher spatial resolution platforms are predicted to have better mapping capabilities. The Quickbird sensor can provide 2.5m spatial resolution with multi-spectral capability. The multi-spectral bands include a blue band for water penetration and a near-infrared band for vegetation detection. Finally, aerial photography flown at low altitude represents the highest spatial resolution (.25m) and can be collected in visible and near-infrared to allow processing of blue and infrared bands. A combination of sensor types to maximize both spatial resolution and spectral signatures may provide the best solution for mapping and monitoring seagrass beds.

Reliable and accurate crop classification maps are an important data source for agricultural monitoring and food security assessment studies. For many years, crop type classification and monitoring were focused on single-source optical satellite data classification. With advancements in sensor technologies and processing capabilities, the potential of multi-source satellite imagery has gained increasing attention. The combination of optical and radar data is particularly promising in the context of crop type classification as it allows explaining the advantages of both sensor types with respect to e.g. vegetation structure and biochemical properties. This review article gives a comprehensive overview of studies on crop type classification using optical and radar data fusion approaches. A structured review of fusion approaches, classification strategies and potential for mapping specific crop types is provided. Finally, the partially untapped potential of radar-optical fusion approaches, research gaps and challenges for upcoming future studies are highlighted and discussed.

Mineral groups identified through automated analysis of remote sensing data acquired by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) were used to generate a map showing the type and spatial distribution of hydrothermal alteration, other exposed mineral groups, and green vegetation across the southwestern conterminous United States. Boolean algebra was used to combine mineral groups identified through analysis of visible, near-infrared, and shortwave-infrared ASTER data into attributed alteration types and mineral classes based on common mineralogical definitions of such types and the minerals present within the mineral groups. Alteration types modeled in this way can be stratified relative to acid producing and neutralizing potential to aid in geoenvironmental watershed studies. This mapping was performed in support of multidisciplinary studies involving the predictive modeling of mineral deposit occurrence and geochemical environments at watershed to ...

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Hyperspectral Remote Sensing Market Size 2024-2028

The hyperspectral remote sensing market size is valued to increase by USD 81 million, at a CAGR of 9.58% from 2023 to 2028. Growing adoption of UAVs will drive the hyperspectral remote sensing market.

Major Market Trends & Insights

North America dominated the market and accounted for a 30% growth during the forecast period.
By Type - VNIR segment was valued at USD 52.20 million in 2022
By Application - Agriculture and forestry segment accounted for the largest market revenue share in 2022

Market Size & Forecast

Market Opportunities: USD 102.85 million
Market Future Opportunities: USD 81.00 million
CAGR from 2023 to 2028 : 9.58%

Market Summary

Hyperspectral remote sensing is an advanced technology that utilizes electromagnetic radiation beyond the visible spectrum to gather detailed information about the earth's surface. The market for this technology is driven by various factors, including the growing adoption of unmanned aerial vehicles (UAVs) for remote sensing applications and the availability of narrower bandwidths that enable more precise data collection. One significant application of hyperspectral remote sensing is in supply chain optimization. For instance, in the agriculture industry, farmers can use this technology to monitor crop health and identify nutrient deficiencies, pests, and diseases in real-time. By addressing these issues promptly, farmers can improve yields and reduce losses.
In fact, a study showed that implementing hyperspectral remote sensing led to a 15% increase in crop yield and a 20% reduction in water usage. Despite its numerous benefits, the high capital investment required for hyperspectral remote sensing systems remains a challenge for market growth. However, as technology advances and costs decrease, more industries are expected to adopt this technology for various applications, including environmental monitoring, mineral exploration, and military intelligence. In conclusion, the hyperspectral remote sensing market is poised for growth due to its ability to provide detailed information about the earth's surface, the increasing use of UAVs, and the availability of narrower bandwidths.
The technology's potential applications are vast, from agriculture to military intelligence, and its ability to improve efficiency and reduce costs makes it an attractive investment for businesses.

What will be the Size of the Hyperspectral Remote Sensing Market during the forecast period?

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How is the Hyperspectral Remote Sensing Market Market Segmented ?

The hyperspectral remote sensing market industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Type

  VNIR
  SWIR
  Thermal LWIR


Application

  Agriculture and forestry
  Geology and mineral exploration
  Ecology
  Disaster management


Geography

  North America

    US


  Europe

    France
    Germany
    UK


  APAC

    China


  Rest of World (ROW)

By Type Insights

The vnir segment is estimated to witness significant growth during the forecast period.

Hyperspectral Remote Sensing is a dynamic and evolving market, driven by advancements in pixel classification and feature extraction techniques. Hyperspectral cameras employ sensor calibration methods for accurate spectral signatures, enabling land cover mapping and vegetation health indices assessment. Data fusion techniques, big data analytics, and image processing algorithms facilitate yield prediction, mineral exploration, and water quality assessment. Cloud computing platforms, spectral unmixing, and hyperspectral imaging offer temporal and spatial resolution improvements. Machine learning models, target detection, and pattern recognition enhance geospatial data analysis.

Precision agriculture, GIS integration, and biophysical parameters measurement are key applications. The VNIR segment, which includes VNIR HSI instruments with CCD or CMOS sensors, dominated the market in 2023. These sensors convert light into electrical charges, amplify, and convert them to digital signals, enabling remote sensing data collection for various industries.

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The VNIR segment was valued at USD 52.20 million in 2018 and showed a gradual increase during the forecast period.

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Regional Analysis

North America is estimated to contribute 30% to the growth of the global market during the forecast period.Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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Passive Remote Sensing Services Market Outlook

The global passive remote sensing services market size was valued at approximately USD 18.5 billion in 2023 and is projected to reach around USD 34.2 billion by 2032, with a compelling CAGR of 7.1% during the forecast period. This growth is primarily driven by the increasing need for accurate and timely data for environmental monitoring and disaster management, coupled with advancements in remote sensing technologies.

One of the primary growth factors for the passive remote sensing services market is the escalating demand for environmental monitoring. As concerns over climate change, deforestation, and pollution rise, organizations and governments globally are increasingly relying on remote sensing data to make informed decisions. Satellite imaging and aerial photography provide critical data that helps in tracking environmental changes, thereby aiding in the formulation of effective policies and mitigation strategies. Furthermore, the advent of sophisticated satellites and sensors has significantly enhanced the accuracy and reliability of remote sensing data, further fueling its adoption.

Agriculture is another key sector driving the growth of the passive remote sensing services market. The agricultural industry is progressively adopting remote sensing technologies to enhance crop management, optimize resource utilization, and increase yield. By providing detailed imagery and data on soil health, crop conditions, and moisture levels, passive remote sensing enables farmers to make informed decisions, thereby improving productivity and sustainability. Additionally, the integration of remote sensing data with other technologies such as IoT and AI is expected to revolutionize precision farming, further boosting market growth.

Disaster management is also a crucial area where passive remote sensing services are making a significant impact. Natural disasters such as floods, hurricanes, and wildfires are becoming more frequent and severe due to climate change. Remote sensing technologies provide critical data for early warning systems, risk assessment, and post-disaster recovery efforts. By enabling real-time monitoring and accurate damage assessment, these technologies play a vital role in mitigating the impact of disasters and enhancing resilience. Governments and international agencies are increasingly investing in remote sensing capabilities to improve disaster preparedness and response, thereby driving market growth.

In terms of regional outlook, North America currently holds the largest share of the passive remote sensing services market, driven by significant investments in space technology, a robust regulatory framework, and the presence of key market players. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, owing to increasing government initiatives for environmental monitoring, agricultural advancements, and disaster management. Rapid urbanization and industrialization in countries like China and India are also contributing to the growing demand for remote sensing services in the region.

Service Type Analysis

Satellite imaging is one of the most prominent service types in the passive remote sensing services market. This technology involves capturing images of the Earth from satellites equipped with high-resolution cameras and sensors. Satellite imaging provides a broad and comprehensive view of large geographic areas, making it invaluable for applications such as environmental monitoring, urban planning, and disaster management. The high demand for satellite imaging services is driven by their ability to provide consistent, accurate, and up-to-date data. Furthermore, advancements in satellite technology, such as higher resolution sensors and increased revisit rates, are enhancing the capabilities and reliability of satellite imaging services.

Aerial photography is another critical segment within the passive remote sensing services market. This service type involves capturing images from aircraft or drones equipped with cameras. Aerial photography provides high-resolution images and can cover specific areas of interest in great detail. It is particularly useful for applications where high spatial resolution is required, such as urban planning, agriculture, and infrastructure monitoring. The increasing use of drones for aerial photography is significantly boosting this market segment. Drones offer flexibility, cost-effectiveness, and the ability to capture images from various altitudes and angles, making them an attractive option for various remote sensing ap

The dataset consists of remoting sensing images with meter- and ten-meter-level spatial resolutions, covering land cover types of soil, water body, rock, vegetation, snow and ice, and man-made objects. It comprises 118 324 ten-meter-level image samples and 29 551 meter-level image samples, with each sample made up of four kinds of data files, including an original satellite image file of the sample (.tif), a sample preview image file (.jpg), a text file of DN values of different spectral bands (.txt), and a metadata file (.xml).

CatCrops_identification is a Python library developed for the early classification of crop types using remote sensing data (Sentinel-2) and ancillary information. It is based on a Transformer model adapted for the analysis of spectral time series with variable length, and it allows the integration of auxiliary data such as the previous year’s crop, irrigation system, cloud cover, elevation, and other geographic features. The library provides tools to download and prepare datasets, train deep learning models, and generate vector maps with plot-level classification. CatCrops_identification includes scripts to automate the entire workflow and offers a public dataset that combines declared and inspected information on crop types in the Lleida region. This approach improves classification accuracy in the early stages of the agricultural season, offering a robust and efficient tool for agricultural planning and water resource management.

Notice to Data Users: The documentation for this data set was provided solely by the Principal Investigator(s) and was not further developed, thoroughly reviewed, or edited by NSIDC. Thus, support for this data set may be limited.This data set consists of a sampling of each type of Hierarchical Data Format version 4 (HDF4) data that are archived at the eight National Aeronautic and Space Administration (NASA) Earth Science Data Centers (ESDCs). The data were sampled for a collaborative study between The HDF Group, the Goddard Earth Sciences Data and Information Services Center (GES-DISC), and the National Snow and Ice Data Center (NSIDC) in order to assess the complex internal byte layout of HDF files. Based on the results of this assessment, methods for producing a map of the layout of the HDF4 files held by NASA were prototyped using a markup-language-based HDF tool. The resulting maps allow a separate program to read the file without recourse to the HDF application programming interface (API). Data products selected for the study, and a table summarizing the results, are available via HTTPS.

MULTI-TEMPORAL REMOTE SENSING IMAGE CLASSIFICATION - A MULTI-VIEW APPROACH VARUN CHANDOLA AND RANGA RAJU VATSAVAI Abstract. Multispectral remote sensing images have been widely used for automated land use and land cover classification tasks. Often thematic classification is done using single date image, however in many instances a single date image is not informative enough to distinguish between different land cover types. In this paper we show how one can use multiple images, collected at different times of year (for example, during crop growing season), to learn a better classifier. We propose two approaches, an ensemble of classifiers approach and a co-training based approach, and show how both of these methods outperform a straightforward stacked vector approach often used in multi-temporal image classification. Additionally, the co-training based method addresses the challenge of limited labeled training data in supervised classification, as this classification scheme utilizes a large number of unlabeled samples (which comes for free) in conjunction with a small set of labeled training data.

The data of land use types in Central Asia comes from the global land cover products of the European Space Agency's Climate Change Initiative, which has high data quality in Central Asia and accurately depicts the annual dynamic change process of lake area. This data includes 22 land use types. By using the IPCC land use classification system, six land use types, including cultivated land, forest land, grassland, town, unused land and water area, are obtained through reclassification, with a spatial resolution of 300 meters. Including land use data of five Central Asian countries (including Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan) in 2000, 2005 and 2015.

The Remote Sensing Satellites Market size was valued at USD 6.5 Billion in 2024 and is projected to reach USD 8.95 Billion by 2032, growing at a CAGR of 8.3% from 2026 to 2032.Global Remote Sensing Satellites Market DriversThe market drivers for the remote sensing satellites market can be influenced by various factors. These may include:Growth in Climate Monitoring Programs: Deployment of remote sensing satellites to gather climate-related data is supported through increased investments in environmental monitoring initiatives.Expansion of Precision Agriculture: Use of satellite-based imaging for crop health, soil condition, and irrigation management is maintained by agricultural sectors to improve land productivity.Demand from Defense and Intelligence: Application of high-resolution imagery and real-time surveillance from satellites is maintained for border monitoring, reconnaissance, and strategic planning.Use in Natural Disaster Management: Remote sensing is applied for early warning, damage assessment, and recovery planning during floods, earthquakes, and wildfires.

The dataset contains maps of the main classes of agricultural land use (dominant crop types and other land use types) in Germany, which have been produced annually at the Thünen Institute beginning with the year 2017 on the basis of satellite data. The maps cover the entire open landscape, i.e., the agriculturally used area (UAA) and e.g., uncultivated areas. The map was derived from time series of Sentinel-1, Sentinel-2, Landsat 8 and additional environmental data. Map production is based on the methods described in Blickensdörfer et al. (2022).

All optical satellite data were managed, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software FORCE - Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019), in which SAR and environmental data were integrated.

The map extent covers all areas in Germany that are defined as agricultural land, grassland, small woody features, heathland, peatland or unvegetated areas according to ATKIS Basis-DLM (Geobasisdaten: © GeoBasis-DE / BKG, 2020).

Version v201:Post-processing of the maps included a sieve filter as well as a ruleset for the reduction of non-plausible areas using the Basis-DLM and the digital terrain model of Germany (Geobasisdaten: © GeoBasis-DE / BKG, 2015). The final post-processing step comprises the aggregation of the gridded data to homogeneous objects (fields) based on the approach that is described in Tetteh et al. (2021) and Tetteh et al. (2023).

The maps are available in FlatGeobuf format, which makes downloading the full dataset optional. All data can directly be accessed in QGIS, R, Python or any supported software of your choice using the provided URL to the datasets (right click on the respective data set --> “copy link address”). By doing so the entire map area or only the regions of interest can be accessed. QGIS legend files for data visualization can be downloaded separately.

Class-specific accuracies for each year are proveded in the respective tables. We provide this dataset "as is" without any warranty regarding the accuracy or completeness and exclude all liability.

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References:Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269, 112831.

BKG, Bundesamt für Kartographie und Geodäsie (2015). Digitales Geländemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 28. April 2022).

BKG, Bundesamt für Kartographie und Geodäsie (2020). Digitales Basis-Landschaftsmodell. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 28. April 2022).

Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124.

Tetteh, G.O., Gocht, A., Erasmi, S., Schwieder, M., & Conrad, C. (2021). Evaluation of Sentinel-1 and Sentinel-2 Feature Sets for Delineating Agricultural Fields in Heterogeneous Landscapes. IEEE Access, 9, 116702-116719.

Tetteh, G.O., Schwieder, M., Erasmi, S., Conrad, C., & Gocht, A. (2023). Comparison of an Optimised Multiresolution Segmentation Approach with Deep Neural Networks for Delineating Agricultural Fields from Sentinel-2 Images. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science

National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021) © 2024 by Schwieder, Marcel; Tetteh, Gideon Okpoti; Blickensdörfer, Lukas; Gocht, Alexander; Erasmi, Stefan; licensed under CC BY 4.0.

Funding was provided by the German Federal Ministry of Food and Agriculture as part of the joint project “Monitoring der biologischen Vielfalt in Agrarlandschaften” (MonViA, Monitoring of biodiversity in agricultural landscapes).

This data provides the basis for the report titled

"Ready for integrated sustainable agricultural land management?

Are practitioners in archaeology and agriculture informed, willing, enabled, and motivated to change how they work with remote and near-surface sensing data to collaboratively address contemporary challenges in sustainable agricultural land management? "

Data were collected in compliance with the University of Glasgow's Research Ethics Policy (Application #100200154).

As stated in the Methods section of this report:

"The participatory survey was conducted between May 2021 and October 2022.

Location: The preponderance of stakeholders engaged with are professional practitioners or researchers based in the UK, Belgium, Italy, Cyprus, Spain and France. Sessions occurred remotely (online/phone), as well as on site, during workshops at the University of Glasgow, the Dalswinton Estate, Dumfries, and Manor Farm, Yedingham.

Participants

Selection: A sub-group of 51 high-level participants were selected from a greater network of 86 stakeholders who were engaged with during the ipaast project.

Sector: Farmers, researchers, heritage managers, geophysicists, remote sensing specialists, statisticians, soil scientists, service providers, sensor developers, and data archivists, who all deal directly, or indirectly with datasets relating to the measurement of soil and/or plant properties (physical, chemical, microbial) were represented (Table 1)

Expertise: Engagement with mid- to late- career specialists was prioritised, with many participants having over 20 years of experience and most having over 10 years of experience (including time during the PhD).

Interview method

Engagement with stakeholders was primarily through one-to-one interviews and structured workshop discussions, conducted either in person, or remotely over video conference or phone. In some instances, participants provided written input (see Table 2 summary). Follow-up interviews or written exchanges were used to clarify or continue discussions when required. A semi-structured approach to interviews and discussions was preferred, with a mix of general questions (see sample questions), as well as questions specifically tailored to the participants specialist background and experience.

Sample Questions:

What types of sensing data do you use/collect?

Where/how do you access/collect these data?

What are your main aims/applications in using or collecting these data?

How often do you access/collect, or anticipate accessing/collecting, these data to be useful to you?

What spatial resolution is necessary for these data to be useful to you?

What, if anything, would encourage/discourage you from sharing your data?

What kinds of additional data types or additional information (metadata) might help you to better understand and use data which you have previously collected or received?

What do you see as the main impacts, if any, of ecosystem service frameworks and/or recent changes to rural/environmental regulations on your work?

What attitudes to sensing data do you see from other stakeholders in rural affairs?

Documentation: Where viable, interviews and workshop discussions were recorded and transcribed; alternatively, notes were made during engagement by either the interviewer and/or dedicated participant observers (e.g. at workshops). Where notes were used, specific quotes and summary reports were checked with the participants for accuracy. "

The Remote Sensing Coastal Change Simple Data Service provides timely and long-term access to emergency, provisional, and approved photogrammetric imagery, derivatives, and ancillary data through a web service via HyperText Transfer Protocol to a folder/file structure organized by data collection platform and survey (collection effort) with metadata sufficient to facilitate both human and machine access. Data are acquired, processed, and published using standardized workflows. Each data type added to the service has a peer-reviewed metadata and data review of sample data generated with standardized methods to ensure compliance with U.S. Geological Survey (USGS) Fundamental Science Practices (FSP).

Description

This dataset consist of two vector files which show the change in the building stock of the City of DaNang retrieved from satellite image analysis. Buildings were first identified from a Pléiades satellite image from 24.10.2015 and classified into 9 categories in a semi-automatic workflow desribed by Warth et al. (2019) and Vetter-Gindele et al. (2019).

In a second step, these buildings were inspected for changes based on a second Pléiades satellite image acquired on 13.08.2017 based on visual interpretation. Changes were also classified into 5 categories and aggregated by administrative wards (first dataset: adm) and a hexagon grid of 250 meter length (second dataset: hex).

The full workflow of the generation of this dataset, including a detailled description of its contents and a discussion on its potential use is published by Braun et al. 2020: Changes in the building stock of DaNang between 2015 and 2017

Contents

Both datasets (adm and hex) are stored as ESRI shapefiles which can be used in common Geographic Information Systems (GIS) and consist of the following parts:

• shp: polygon geometries (geometries of the administrative boundaries and hexagons)
• dbf: attribute table (containing the number of buildings per class for 2015 and 2017 and the underlying changes (e.g. number of new buildings, number of demolished buildings, ect.)
• shx: index file combining the geometries with the attributes
• cpg: encoding of the attributes (UTF-8)
• prj: spatial reference of the datasets (UTM zone 49 North, EPSG:32649) for ArcGIS
• qpj: spatial reference of the datasets (UTM zone 49 North, EPSG:32649) for QGIS
• lyr: symbology suggestion for the polygons(predefined is the number of local type shophouses in 2017) for ArcGIS
• qml: symbology suggestion for the polygons (predefined is the number of new buildings between 2015 and 2017) for QGIS

Citation and documentation

To cite this dataset, please refer to the publication

• Braun, A.; Warth, G.; Bachofer, F.; Quynh Bui, T.T.; Tran, H.; Hochschild, V. (2020): Changes in the Building Stock of Da Nang between 2015 and 2017. Data, 5, 42. doi:10.3390/data5020042

This article contains a detailed description of the dataset, the defined building type classes and the types of changes which were analyzed. Furthermore, the article makes recommendations on the use of the datasets and discusses potential error sources.

Semantic technology plays a key role in various domains, from conversation understanding to algorithm analysis. As the most efficient semantic tool, ontology can represent, process and manage the widespread knowledge. Nowadays, many researchers use ontology to collect and organize data's semantic information in order to maximize research productivity. In this paper, we firstly describe our work on the development of a remote sensing data ontology, with a primary focus on semantic fusion-driven research for big data. Our ontology is made up of 1,264 concepts and 2,030 semantic relationships. However, the growth of big data is straining the capacities of current semantic fusion and reasoning practices. Considering the massive parallelism of DNA strands, we propose a novel DNA-based semantic fusion model. In this model, a parallel strategy is developed to encode the semantic information in DNA for a large volume of remote sensing data. The semantic information is read in a parallel and bit-wise manner and an individual bit is converted to a base. By doing so, a considerable amount of conversion time can be saved, i.e., the cluster-based multi-processes program can reduce the conversion time from 81,536 seconds to 4,937 seconds for 4.34 GB source data files. Moreover, the size of result file recording DNA sequences is 54.51 GB for parallel C program compared with 57.89 GB for sequential Perl. This shows that our parallel method can also reduce the DNA synthesis cost. In addition, data types are encoded in our model, which is a basis for building type system in our future DNA computer. Finally, we describe theoretically an algorithm for DNA-based semantic fusion. This algorithm enables the process of integration of the knowledge from disparate remote sensing data sources into a consistent, accurate, and complete representation. This process depends solely on ligation reaction and screening operations instead of the ontology.