#geospatialanalytics

[nschool]

How Data Science is Helping Fight Climate Change

Climate change is no longer a distant threat—it’s a reality affecting ecosystems, economies, and everyday lives. From rising sea levels to extreme weather events, the impact is global. But there’s a powerful tool helping scientists, policymakers, and activists respond more effectively: Data Science.

With the explosion of big data, sensors, satellites, and machine learning algorithms, data science is becoming a central force in the fight against climate change. Let’s explore how.

1. Predicting Climate Patterns with Machine Learning

One of the most powerful applications of data science is in climate modeling and forecasting. Traditional models were limited in processing power and granularity. Now, with advanced machine learning techniques and high-performance computing, scientists can:

Simulate climate changes decades into the future

Predict weather patterns more accurately

Model extreme events like hurricanes, floods, or droughts

For example, DeepMind’s AI model, trained on vast datasets of radar data, can now predict rainfall with higher precision than traditional methods. These forecasts help communities prepare for disasters and reduce damage.

2. Satellite Imagery and Earth Observation

Satellites continuously gather images and climate data from space. These images are rich with information—about deforestation, glacier melting, ocean temperatures, and more.

Data scientists use image recognition and geospatial analytics to:

Monitor forest cover loss in the Amazon

Track ice sheet melting in Antarctica

Identify urban heat islands in growing cities

Measure carbon emissions from industrial zones

Organizations like NASA, ESA, and Google Earth Engine are publishing petabytes of open climate data for researchers to build models, apps, and solutions.

3. Carbon Footprint Analysis

Governments and companies are under increasing pressure to reduce their carbon footprints. But first, they need to measure them accurately.

Data science enables:

Carbon accounting across supply chains

IoT integration in factories for real-time emission tracking

Predictive models to simulate the impact of green policies

For instance, companies like Microsoft and Apple are using advanced analytics to reduce their net carbon emissions and optimize energy use across data centers.

4. Climate-Smart Agriculture

Agriculture is both a victim and a contributor to climate change. Data science is helping farmers adapt through climate-smart agriculture practices:

Yield prediction using historical and weather data

Soil health monitoring through sensors and analytics

Pest and disease detection using AI-driven image classification

Precision irrigation to reduce water usage

Platforms like IBM’s Watson Decision Platform for Agriculture use AI to give farmers insights that boost productivity while reducing environmental impact.

5. Greener Cities with Smart Data

Urban areas contribute heavily to CO₂ emissions. With smart data collected from sensors, traffic cams, GPS, and public utilities, data scientists help cities become more sustainable:

Optimizing public transport to reduce fuel consumption

Monitoring air quality in real-time

Planning green spaces using heat maps

Managing waste and recycling more efficiently

Cities like Singapore, Amsterdam, and San Francisco are already leading the way in becoming “smart cities,” using data science to reduce emissions and improve quality of life.

6. Renewable Energy Optimization

The shift to solar, wind, and hydro power brings new challenges: fluctuating outputs, grid integration, and energy storage. Here’s where data science steps in:

Forecasting sunlight and wind speeds to predict energy generation

Optimizing battery storage and distribution

Balancing supply and demand across the smart grid

AI models from companies like Google DeepMind have already improved the output prediction of wind farms by up to 20%.

7. Climate Research and Citizen Science

Open-source projects and platforms allow anyone to contribute to climate research. Data scientists use crowd-sourced data to:

Map plastic waste in oceans

Collect wildlife migration data

Record local temperature anomalies

Tools like Zooniverse, Kaggle, and Climate Central invite data scientists and enthusiasts to work on real-world climate datasets and challenges.

8. Policy and Decision-Making Support

Data science doesn't just help collect and analyze data—it also helps governments make better decisions.

Predictive models simulate the outcome of climate policies

Visualization tools make complex data easier for decision-makers to understand

Data-driven reports guide investments in green technologies

The Intergovernmental Panel on Climate Change (IPCC), for example, uses advanced data analytics to build global climate reports that influence international treaties and agreements.

Challenges and Ethical Considerations

While data science offers powerful tools, it also comes with challenges:

Data privacy in sensor-based tracking

Biases in datasets or algorithms

Digital divide, where developing countries may lack infrastructure for data collection

Data scientists must follow ethical guidelines and ensure inclusive, transparent, and responsible use of technology in climate work.

Conclusion: The Role of Data Scientists in a Greener Future

Climate change is a complex, urgent problem—but data science gives us the power to understand, predict, and act.

As a data scientist, you're not just crunching numbers. You're helping to:

Save forests

Reduce emissions

Optimize energy use

Protect communities

Shape global policies

It’s a field where technology meets responsibility. And in the climate battle, every line of clean, purposeful code matters.

[govindhtech]

Earth Engine in BigQuery: A New Geospatial SQL Analytics

BigQuery Earth Engine

With Earth Engine directly integrated into BigQuery, Google Cloud has expanded its geographic analytics capabilities. Incorporating powerful raster analytics into BigQuery, this new solution from Google Cloud Next '25 lets SQL users analyse satellite imagery-derived geographical data.

Google Cloud customers prefer BigQuery for storing and accessing vector data, which represents buildings and boundaries as points, lines, or polygons. Earth Engine in BigQuery is suggested for processing and storing raster data like satellite imagery, which encodes geographic information as a grid of pixels with temperature, height, and land cover values.

“Earth Engine in BigQuery” mixes vector and raster analytics. This integration could improve access to advanced raster analysis and help solve real-world business problems.

Key features driving this integration:

BigQuery's new geography function is ST_RegionStats. This program extracts statistics from raster data inside geographic borders, similar to Earth Engine's reduceRegion function. Use an Earth Engine-accessible raster picture and a geographic region (vector data) to calculate mean, min, max, total, or count for pixels that traverse the geography.

BigQuery Sharing, formerly Analytics Hub, now offers Earth Engine in BigQuery datasets. This makes it easy to find data and access more datasets, many of which are ready for processing to obtain statistics for a region of interest. These datasets may include risk prediction, elevation, or emissions.

Raster analytics with this new feature usually has five steps:

Find vector data representing interest areas in a BigQuery table.

Find an Earth Engine raster dataset in BigQuery image assets, Cloud GeoTiff, or BigQuery Sharing.

Use ST_RegionStats() with the raster ID, vector geometries, and optional band name to aggregate intersecting data.

To understand, look at ST_RegionStats() output.

Use BigQuery Geo Viz to map analysis results.

This integration enables data-driven decision-making in sustainability and geographic application cases:

Climate, physical risk, and disaster response: Using drought, wildfire, and flood data in transportation, infrastructure, and urban design. For instance, using the Wildfire hazard to Communities dataset to assess wildfire risk or the Global River Flood Hazard dataset to estimate flood risk.

Assessing land-use, elevation, and cover for agricultural evaluations and supply chain management. This includes using JRC Global Forest Cover datasets or Forest Data Partnership maps to determine if commodities are grown in non-deforested areas.

Methane emissions monitoring: MethaneSAT L4 Area Sources data can identify methane emission hotspots from minor, distributed sources in oil and gas basins to enhance mitigation efforts.

Custom use cases: Supporting Earth Engine raster dataset imports into BigQuery image assets or Cloud Storage GeoTiffs.

BigQuery Sharing contains ST_RegionStats()'s raster data sources, where the assets.image.href column normally holds the raster ID for each image table. Cloud Storage GeoTIFFs in the US or US-central1 regions can be used with URIs. Earth Engine image asset locations like ‘ee://IMAGE_PATH’ are supported in BigQuery.

ST_RegionStats()'s include option lets users adjust computations by assigning pixel weights (0–1), with 0 representing missing data. If no weight is given, pixels are weighted by geometry position. Raster pixel size, or scale, affects calculation and output. Changing scale (e.g., using options => JSON ‘{“scale”: 1000}’) can reduce query runtime and cost for prototyping, but it may impact results and should not be used for production analysis.

ST_RegionStats() is charged individually under BigQuery Services since Earth Engine calculates. Costs depend on input rows, raster picture quality, input geography size and complexity, crossing pixels, image projection, and formula usage. Earth Engine quotas in BigQuery slot time utilisation can be changed to control expenses.

Currently, ST_RegionStats() queries must be run in the US, us-central1, or us-central2.

This big improvement in Google Cloud's geospatial analytics provides advanced raster capabilities and improves sustainability and other data-driven decision-making.

[metastatblog]

Geospatial Analytics Market Analysis, Size, Share, Growth, Trends, and Forecasts by 2031

The Global Geospatial Analytics market is poised at the cusp of revolutionary change as it finds its place at the core of many industries. This will span everything from basic usage of data from geographic locations and features to expanded influence within the next couple of years. Technology and demand for spatial data decision-making drive the growth; the industry is moving to support much more advanced applications and use cases. 

𝐆𝐞𝐭 𝐚 𝐅𝐫𝐞𝐞 𝐒𝐚𝐦𝐩𝐥𝐞 𝐑𝐞𝐩𝐨𝐫𝐭:https://www.metastatinsight.com/request-sample/2931

Companies

Alteryx, Inc.

Bentley Systems Incorporated

ESRI, Inc.

Fugro N.V.

General Electric Company

Google LLC

Foursquare

Trimble Inc.

Maxar Technologies Inc.

SAP SE

T𝐡𝐞 𝐅𝐮𝐥𝐥 𝐑𝐞𝐩𝐨𝐫𝐭:https://www.metastatinsight.com/report/geospatial-analytics-market

Behind this growth and development in the market, there is a steady improvement of geospatial technologies. Further advances in satellite imagery, GIS, and remote sensing will revolutionize the way geographic data is captured, analyzed, and utilized. Ultimately, these technologies will be even more integrated into daily applications such as urban planning, infrastructure construction, environmental monitoring, and disaster management. This will further fuel the drive towards the advancement of geospatial analytics tools and solutions that can show more profound insight with better predictions. 

Businesses across different industries will look at leveraging geospatial analytics to stay ahead of the competition. This ranges from using geospatial insights in store location optimization, supply chain optimization, and targeted marketing based on geographic trends. Similarly, in the agricultural sector, more spatial data will be employed to improve crop management, monitor soil health, and predict weather conditions to create sustainable farming techniques. The healthcare sector will also continue its use of geospatial analytics in tracking outbreaks of diseases, in planning facilities pertaining to healthcare, and enhancing patient care by integrating spatial data. 

Technological change, increased demand for spatial data, and the need for an integrated solution are just some of the factors that will be driving this market at an exponential rate. The boom, innovations, and opening of a new dimension for opportunities and insights that mark the future course in making data-driven decisions-so goes the prediction as industries and sectors globally start realizing the worth of geospatial analytics. 

Global Geospatial Analytics market is estimated to reach $247.3 billion by 2031; growing at a CAGR of 13.2% from 2024 to 2031.

Contact Us:   

inquiry@metastatinsight.com

+1 214 613 5758

[daintilyultimateslayer]

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[geoscience123]

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[gis56]

AI for Satellite Imagery: $10.9B Market by 2034 🚀 (15.9% Growth)

AI for Analyzing Satellite Imagery Market : The integration of Artificial Intelligence (AI) in satellite imagery analysis is revolutionizing geospatial intelligence by enabling faster, more accurate, and scalable insights. AI-powered models, including machine learning (ML) and deep learning, enhance image processing, object detection, and change detection, making satellite data more actionable. Industries such as agriculture, defense, disaster management, and urban planning leverage AI-driven satellite analysis to monitor landscapes, predict environmental changes, and optimize resource allocation. With AI automating vast datasets in real-time, decision-makers gain unparalleled visibility into global trends, empowering smarter strategies and proactive responses.

To Request Sample Report: https://www.globalinsightservices.com/request-sample/?id=GIS10828 &utm_source=SnehaPatil&utm_medium=Article

The applications of AI in satellite imagery extend beyond traditional mapping. In agriculture, AI assists in crop health monitoring, irrigation management, and yield prediction, improving food security. In climate science, AI-driven models analyze ice cap melting, deforestation rates, and natural disasters, providing early warnings and mitigation strategies. The defense and security sector benefits from AI-powered surveillance, tracking unauthorized activities, and ensuring national security. Moreover, AI optimizes disaster response by detecting affected areas faster, allowing authorities to deploy aid more efficiently. The convergence of AI and satellite technology is setting new benchmarks in precision, efficiency, and automation, transforming the way industries leverage Earth observation data.

The future of AI-driven satellite imagery analysis is promising, with advancements in neural networks, edge computing, and cloud-based analytics enhancing its capabilities. As AI algorithms improve, the accuracy of detecting anomalies, classifying objects, and predicting trends will reach new heights. With growing investments in AI, geospatial analytics, and remote sensing, the market is poised for exponential growth. The demand for real-time, AI-enhanced satellite intelligence will continue to drive innovation, reshaping industries and fostering sustainable development worldwide.

#AI #SatelliteImagery #GeospatialAI #MachineLearning #EarthObservation #RemoteSensing #BigData #DeepLearning #SpaceTech #GIS #AgricultureAI #UrbanPlanning #DisasterManagement #ClimateChange #SmartFarming #AIInnovation #SurveillanceTech #GeospatialAnalytics #AIinDefense #EnvironmentalMonitoring #NeuralNetworks #CloudComputing #EdgeAI #SpaceExploration #AIAutomation #TechForGood #AIRevolution #SmartCities #AIinAgriculture #FutureTech

[newspatron]

Airtel & Google Cloud Soar: 10 Ways Businesses Benefit

Have you explored the Airtel Google Cloud partnership yet? Share your thoughts and questions below!

A New Chapter for Indian Businesses: The Airtel and Google Cloud Alliance Airtel #GoogleCloud #CloudComputing #AI #India #Business #Technology #Partnership #Innovation #Growth #GeospatialAnalytics #VoiceAnalytics #MarketingTechnology #IoT #DigitalTransformationCloud Computing: The Engine Powering India’s Business BoomAirtel: Empowering Indian BusinessesGoogle Cloud: Global Innovation, Local…

[mykiranposts]

With many years of research and real life experience in all territories of the #GeographicalInformationSystem (GIS) domain, we provide successful and accurate execution of your GIS related projects. For any research project, the essential and important point with #GISmappingdata is registering a map with the correct real world coordinates. If the maps are not #georeferenced, no other information can be displayed over or positioned under the map coverage. With our GIS services, we offer accurate #georeferencing / geocoding of your projects at affordable prices. We are capable of various #geospatialanalytical procedures for users in agriculture, transport, electrical network, water supplies, sewage, etc.

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[savibangar]

[your-abhijit-shelke-fan-blog]

Geospatial Analytics Market will experience the highest growth rate of 16.21% during the given forecast period 2014 – 2023

Competitive Insights: The major players in the market are Hexagon AB, Harris Corporation, Bentley Systems, SAP SE, DigitalGlobe, Esri, Maxar Technologies, Trimble, RMSI Private Limited, Oracle Corporation, Alteryx, Fugro and General Electric. The major players in the market are profiled in detail in view of qualities, for example, company portfolio, business strategies, financial overview, recent developments, and share of the overall industry.

Get Sample Copy of This Report @ https://www.crystalmarketresearch.com/report-sample/IC06927 .

The Geospatial Analytics Market is segmented as follows-

By Component: Services Software & Solutions

By Type: Network & Location Analytics Surface & Field Analytics Geovisualization Others

By Application: Medicine & Public Safety Climate Change Adaptation Surveying Disaster Risk Reduction & Management Others

By Vertical: Automotive Government Natural Resources Business Utility & Communication Defence & Intelligence Other

Industry Outlook and Trend Analysis: The Geospatial Analytics Market was worth USD 22.42 billion in 2014 and is expected to reach approximately USD 86.71 billion by 2023, while registering itself at a compound annual growth rate (CAGR) of 16.21% during the forecast period.

The market is anticipated to witness a substantial development amid the gauge time frame, significantly determined by the expanding usage of Internet of Things (IoT), integration of geospatial technology with standard technology, and major progressions in geospatial investigation with the advent of artificial intelligence and big data. With the advent of big data and artificial intelligence in the market, geospatial analytics can give enhanced and more affordable geospatial data to organizations over the globe. These solutions give extra advantages, for example, the capacity to serve on-demand analytics, expanded availability and capacity to analyse complex and large datasets, and investigating various sorts of geospatial data through cloud-based geospatial analytics.

For more information, click on the below link @ https://www.crystalmarketresearch.com/report/geospatial-analytics-market .

Table of Contents: 1.Introduction 2.Executive Summary 3.Market Overview 4.Market Analysis by Regions 4.1.North America (United States, Canada and Mexico) 4.2.Europe (Germany, France, UK, Russia, Italy and Rest of Europe) 4.3.Asia-Pacific (China, Japan, Korea, India, Southeast Asia and Rest of Asia-Pacific) 4.4.South America (Brazil, Argentina, Columbia and Rest of South America) 4.5.Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria, South Africa and Rest of MEA) 5.Geospatial Analytics Market, By Component 5.1.Introduction 5.2.Global Geospatial Analytics Revenue and Market Share by Component (2014-2018) 5.3.Software & Solutions 5.4.Services To be Continued...

Regional Outlook and Trend Analysis: North America is assessed to be the biggest market for geospatial analytics over the forecast period. The development of the geospatial analytics market in North America is essentially driven by the high usage of most recent technologies, for example, distributed computing, huge information, Internet of Things (IoT), Artificial Intelligence (AI), and business intelligence tools.

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