You're faced with conflicting remote sensing data and GIS layers. How do you resolve the discrepancies?

Remote Sensing data provides us the advantage of getting the latest information regarding the land use and land cover. The GIS data, on the other hand is not so dynamic. My student and I have written a naive algorithm to generate segments from remotely sensed data, and we are working towards achieving mathematical rigour and a better overall accuracy. The technique of updating the GIS layers based on the outputs of our algorithm will be a challenge, but that is what I am working on right now.

We can start by assessing the accuracy of each dataset. Begin with ground truthing, where field data collected on site is used as references. Compare this field data against the remote sensing data and GIS layers to identify errors. Next, evaluate the metadata of each dataset for details on accuracy, resolution, and date of capture, as newer data might be more reliable. Use statistical methods like root mean square error (RMSE) to quantify the differences between the datasets and the ground truth. Finally, cross-validate the datasets with additional sources or expert knowledge to identify the most accurate information. By systematically analyzing these factors, we can determine which dataset is more reliable.

Na minha experiência percebo que é importantíssimo verificar o DATUM das camadas. Muitas vezes, apenas reprojetar as camadas para o mesmo DATUM pode resolver uma série de "problemas" ao iniciar o seu projeto.

Consider factors like sensor type, acquisition date, and processing methods. Employ data validation techniques like ground truthing or statistical analysis to identify discrepancies and assess the reliability of each source. Identify necessary map layers and tools, make a map to share analysis results & apply filters and query expressions,etc. Capturing accurate data is crucial if you want to make sound business decisions. To assess the accuracy of both the remote sensing data and the GIS layers. This step is about being selective and ensuring that the foundation of your GIS model is built on the best possible data.

The first task in GIS should be to examine the base data. Then prepare or digitize layers using proper tools, and conduct a two-level quality control (QC) process, followed by a final quality assurance (QA) check. I am confident that the output product will be accurate.

First, always make sure you know where the GIS layer came from and what year it was made with reference to satellite imagery. You can check it via metadata or contact the data provider. If this is not possible, you can carry out a visual inspection and comparison of several features that may not change for a long time with satellite imagery, for example, large bodies of water or major roads. Differences in satellite sensors, image processing algorithms, or classification criteria can lead to discrepancies. Make sure that you're using consistent methods.

Finding a related ground rules is as important as getting the data itself, it means that you should be aware of: - Get data types that can integrate together in a GIS software. - If Many Raster, try to rescale your data into a unified classification technique using raster calculations and geoprocessing tools. - Use math ?? calculations to find a related output numbers that can produce an information or identify a trend. - Record your work steps to give you the ability to get back if needed.

As a geologist who works in petroleum geology research, GIS is a critical tool that can help you gather a lot of information from other authors and overlay as much as you want. Of course, being sure of locations in GIS for Georeferencing is critical. 1. Check your projection and always be aware of it. It can distort your maps. 2. If something is difficult to Geo-Reference, you will always find a clue of something else that you can Geo-Reference first to give you a better view. 3. Compare your maps and lengths to ensure that all the work you do fits correctly with your Georeferencing.

Para conciliar capas, considera estos puntos: -Proyección y coordenadas: Alinea todas las capas en la misma proyección y sistema de coordenadas. -Atributos: Asegura la consistencia y compatibilidad de atributos y clasificaciones. -Clasificación de píxeles: Ajustar la clasificación de los píxeles en las imágenes de teledetección para reflejar mejor las categorías. -Verificación: Valida la precisión con datos de terreno y fuentes públicas oficiales.

In 2008, during my master’s thesis at INPE, I ventured into new territory in geoprocessing. At that time, the standard approach was to align all GIS data to the same spatial and temporal resolutions. My former supervisor, Silvana Amaral, proposed a highly unconventional method: using a cellular grid to integrate variables from various sources, each with different spatial and temporal resolutions, however at the same geographical coordinates. This approach, though unusual back then, was applied to map the ecoregions of the Purus-Madeira interfluve in the Amazon. Today, this method is gaining traction in some studies, demonstrating how innovative techniques can eventually influence broader practices in environmental mapping.

Reconciling conflicting data requires evaluating the reliability of each dataset for the specific application. If remote sensing data offers superior currency and resolution, updating GIS layers is often optimal. Conversely, for GIS layers grounded in detailed, verified information, modifying remote sensing data through pixel reclassification or vector adjustment to match known ground truth might be necessary. Key Improvements: Clarity and conciseness: The text is more direct and easier to understand. Specificity: The term "ground-truthed information" is more precise. Sentence structure: The sentence structure is slightly adjusted for better flow.

Date Matching: Ensure that the data being compared are from the same time period. Differences in time can result in changes in the landscape that are reflected differently in each dataset.

I think for a better reconciliation of the Layers we need to develop automated tools that can use the raster data collected by remote sensing to get out the GIS layers. That can assure an uniformity of data.

Make sure to Identify Experts in the field and Reach out to professionals with experience in remote sensing and GIS. Consider those who have a strong track record in resolving data discrepancies. Develop with them a Collaborative Workshops which can be super helpful, as to organise workshops where these experts can analyse the conflicting data together, sharing their insights and proposing solutions.

It is always good to be in a circle of people or community of experts that you can consult, not only when you need it but to constantly check-in and learn new things

Experts in different dataset usages usually can walkthrough steps of aligning datasets together and can spot on where things usually go wrong according to accumulated experiences with such datasets or environments.

Improved Text: Persistent discrepancies may necessitate expert intervention. Consulting professionals with expertise in both remote sensing and GIS can provide valuable insights into error sources and potential solutions. This might involve re-evaluating data collection methods, exploring alternative software or analysis techniques, or collecting new data to enhance accuracy. Key Improvements: Conciseness: The text is more concise while retaining essential information. Clarity: The language is more direct and easier to understand. Specificity: The term "expert intervention" is more precise.

Utiliser les outils appropriés est essentiel pour optimiser l'analyse des données SIG. Les logiciels SIG avancés, tels qu'ArcGIS, QGIS et ERDAS IMAGINE, offrent des fonctionnalités puissantes pour traiter, visualiser et analyser des données géospatiales complexes. Exploitez des outils de télédétection, de recalibration et de fusion de données pour harmoniser les couches et corriger les incohérences. Les algorithmes de classification et les techniques de traitement d'image permettent d'améliorer la précision des analyses. L'utilisation de modèles de données avancés et de scripts automatisés facilite l'intégration et la gestion des données à grande échelle. Ma?triser ces outils garantit des résultats fiables et des décisions éclairées.

Improved Text: Utilize your GIS software's built-in tools to reconcile discrepancies. Data conflation, a process of combining datasets for improved accuracy, is often available. Additionally, spatial adjustment tools can align features from different sources by using control points or reference data. Key Improvements: Conciseness: The text is more concise while retaining essential information. Clarity: The language is more direct and easier to understand. Action-oriented: The revised text encourages the user to take action.

Learn from this experience to enhance data management practices and prevent future discrepancies. Implement regular updates of GIS layers with the latest remote sensing data. Incorporate robust quality control measures, including peer reviews and automated checks, to identify errors early. Continuous process improvement and staying abreast of technological advancements will significantly reduce the risk of geospatial data inconsistencies. Key improvements: More concise and direct language. Emphasizes the importance of learning from the experience. Stronger call to action for implementing improvements.

AI-Driven Monitoring System and AI Surveillance can be helpful, thus develop an AI-driven monitoring system that continuously scans remote sensing data and GIS layers for discrepancies. The system can alert stakeholders in real time when issues are detected. In this context, Adaptive Learning, enables the AI to learn from each discrepancy resolution, improving its accuracy and efficiency over time.

We can also use mathematical predictions to forecast the evolution of discrepancies. By using precedent discrepancies between GIS layers and Remote sensing data we can get out an evolution of discrepancies. This can be helpful to adjust GIS classification by training the computer to take into account precedent errors and minimise those discrepancies by the end.

First I will compare their metadata, the spatial extent and the projection. If there is a mismatch in the comparison or sometimes missing information in their metadata, then you need to start working on those differences first and adjust to the setting that fit to your projects and objectives. To solve this, you can try to do things like project transformation or convert them to prereferable environment setting. If you used ArcGIS Pro you can set the environment for all the dataset to be the same in the environnment tab.

Differences in coordinate system is a leading course of discrepancy in data. It would help if we check that the Gis layer and remotely sensed data is in the same projection

In this case first I try to find a third resource or visit some sampling with field work for checking quality of data. After I find the problem, I will choose the method to fix the problem.

Record Findings: Document all findings, the steps taken to resolve discrepancies, and any assumptions made during the process. Report Discrepancies: Clearly report any remaining discrepancies and the potential reasons for them.