subject stringclasses 1
value | paper_id stringclasses 5
values | paper_title stringclasses 5
values | question_id stringclasses 12
values | question stringclasses 12
values | choices dict | answer stringclasses 6
values | metadata dict |
|---|---|---|---|---|---|---|---|
Earth Science - Remote Sensing | 1 | Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method | Q1 | What is the number of land-cover / land-use classes classified in this study? | {
"A": "3",
"B": "6",
"C": "9",
"D": "10",
"E": "All of above",
"F": "None of above"
} | D | {
"Task-oriented Category": "Study subject & experimental setup",
"question_key_term": "land cover",
"term_explanation": "The total number of land cover or land use categories defined for classification. This reflects the complexity of the classification task and determines how fine-grained the land surface repre... |
Earth Science - Remote Sensing | 1 | Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method | Q2 | What is the spatial extent of the study area? | {
"A": "16,411 km²",
"B": "26,035 km²",
"C": "200,000 km²",
"D": "1,419,530 km²",
"E": "All of above",
"F": "None of above"
} | A | {
"Task-oriented Category": "Study subject & experimental setup",
"question_key_term": "geographic area",
"term_explanation": "The geographic area covered by the study, typically measured in square kilometers. This affects the generalizability and computational complexity of the study and can range from local sit... |
Earth Science - Remote Sensing | 1 | Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method | Q3 | What is the geographic type of the study area? | {
"A": "Urban",
"B": "Rural",
"C": "Mixed",
"D": "Natural (e.g., forest, wetland, desert)",
"E": "All of above",
"F": "None of above"
} | A | {
"Task-oriented Category": "Study subject & experimental setup",
"question_key_term": "geographic type",
"term_explanation": "The geographic type or functional nature of the study region. Common categories include urban, rural, natural ecosystems (e.g., forest, wetland), or mixed types. The geographic context i... |
Earth Science - Remote Sensing | 1 | Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method | Q4 | What is the temporal scope of the data used? | {
"A": "Single-scene imagery",
"B": "Short-term imagery ( ≤3 months)",
"C": "Mid-term imagery ( >3 and ≤12 months)",
"D": "Long-term imagery ( >1 year)",
"E": "All of above",
"F": "None of above"
} | C | {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "time span",
"term_explanation": "The total time span covered by the remote sensing imagery used in the study. This could be a single image, a short-term period (weeks to months), seasonal cycles, or long-term historical records... |
Earth Science - Remote Sensing | 1 | Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method | Q5 | What type of remote sensing data is used? | {
"A": "Optical",
"B": "SAR",
"C": "LiDAR",
"D": "Multisource fusion",
"E": "All of above",
"F": "None of above"
} | A | {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "data type",
"term_explanation": "The primary data modality employed in the study. Categories include optical imagery, synthetic aperture radar (SAR), LiDAR, or combinations of multiple sources. The data type determines the spec... |
Earth Science - Remote Sensing | 1 | Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method | Q6 | Which specific satellite data is used? | {
"A": "Sentinel-2",
"B": "Sentinel-1",
"C": "Luojia-1",
"D": "Sentinel-2 and Luojia-1",
"E": "All of above",
"F": "None of above"
} | D | {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "satellite",
"term_explanation": "The name(s) of satellite missions providing the source imagery, such as Sentinel-2, Landsat-8, Sentinel-1, MODIS, or commercial platforms like Planet or WorldView. This helps determine resolutio... |
Earth Science - Remote Sensing | 1 | Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method | Q7 | What is the spatial resolution of the primary imagery used? | {
"A": "10 m",
"B": "16 m",
"C": "27 m",
"D": "1000 m",
"E": "All of above",
"F": "None of above"
} | A | {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "spatial resolution",
"term_explanation": "The ground sampling distance (in meters) of the input imagery, indicating the size of the smallest detectable object. High-resolution imagery (e.g., <5 m) allows fine-scale mapping, whi... |
Earth Science - Remote Sensing | 1 | Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method | Q8 | Are auxiliary features used beyond raw spectral bands? | {
"A": "Vegetation indices (e.g., NDVI)",
"B": "Water features (e.g., NDWI)",
"C": "Vegetation indices and Water indices",
"D": "Elevation / DEM",
"E": "All of above",
"F": "None of above"
} | C | {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "features",
"term_explanation": "Whether the study utilizes additional derived features such as vegetation indices (NDVI, LAI, FAPAR), land surface temperature, albedo, emissivity, topographic information (e.g., DEM), or other e... |
Earth Science - Remote Sensing | 1 | Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method | Q9 | What type of model is implemented in this study? | {
"A": "SVM",
"B": "RF",
"C": "XGBoost",
"D": "CNN",
"E": "All of above",
"F": "None of above"
} | B | {
"Task-oriented Category": "Technical approach & details",
"question_key_term": "ML model",
"term_explanation": "The classification model used to perform land cover or land use mapping. Options include traditional machine learning algorithms (e.g., random forest, SVM), deep learning architectures (e.g., CNNs, U-... |
Earth Science - Remote Sensing | 1 | Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method | Q10 | What performance metrics are reported? | {
"A": "Overall Accuracy (OA)",
"B": "F1-score",
"C": "Kappa",
"D": "OA and Kappa",
"E": "All of above",
"F": "None of above"
} | D | {
"Task-oriented Category": "Technical approach & details",
"question_key_term": "performance metrics",
"term_explanation": "The quantitative indicators used to evaluate model performance, such as overall accuracy (OA), F1-score, Kappa coefficient, and mean intersection-over-union (mIoU). The selection of metrics... |
Earth Science - Remote Sensing | 1 | Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method | Q11 | Is any comparative analysis included? | {
"A": "Compared with traditional classifiers (e.g., RF, SVM)",
"B": "Compared with deep models (e.g., U-Net variants)",
"C": "Ablation study",
"D": "No comparison reported",
"E": "All of above",
"F": "None of above"
} | D | {
"Task-oriented Category": "Conclusions & results",
"question_key_term": "comparative analysis",
"term_explanation": "Whether the study conducts comparisons with baseline models, traditional classifiers, or variants of the proposed method (e.g., ablation study, benchmark comparison). This provides insight into m... |
Earth Science - Remote Sensing | 1 | Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method | Q12 | What is the reported overall accuracy (OA)? | {
"A": "69%",
"B": "74%",
"C": "77%",
"D": "98%",
"E": "All of above",
"F": "None of above"
} | C | {
"Task-oriented Category": "Conclusions & results",
"question_key_term": "overall accuracy",
"term_explanation": "The percentage of correctly classified samples over the total number of samples, as reported in the study. OA is a commonly used metric for remote sensing classification and provides a general measur... |
Earth Science - Remote Sensing | 2 | Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018 | Q1 | What is the number of land-cover / land-use classes classified in this study? | {
"A": "5",
"B": "12",
"C": "21",
"D": "37",
"E": "All of above",
"F": "None of above"
} | B | {
"Task-oriented Category": "Study subject & experimental setup",
"question_key_term": "land cover",
"term_explanation": "The total number of land cover or land use categories defined for classification. This reflects the complexity of the classification task and determines how fine-grained the land surface repre... |
Earth Science - Remote Sensing | 2 | Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018 | Q2 | What is the spatial extent of the study area? | {
"A": "7,317 km²",
"B": "41,576 km²",
"C": "67,558 km²",
"D": "166,338 km²",
"E": "All of above",
"F": "None of above"
} | D | {
"Task-oriented Category": "Study subject & experimental setup",
"question_key_term": "geographic area",
"term_explanation": "The geographic area covered by the study, typically measured in square kilometers. This affects the generalizability and computational complexity of the study and can range from local sit... |
Earth Science - Remote Sensing | 2 | Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018 | Q3 | What is the geographic type of the study area? | {
"A": "Urban",
"B": "Rural",
"C": "Mixed",
"D": "Natural (e.g., forest, wetland, desert)",
"E": "All of above",
"F": "None of above"
} | A | {
"Task-oriented Category": "Study subject & experimental setup",
"question_key_term": "geographic type",
"term_explanation": "The geographic type or functional nature of the study region. Common categories include urban, rural, natural ecosystems (e.g., forest, wetland), or mixed types. The geographic context i... |
Earth Science - Remote Sensing | 2 | Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018 | Q4 | What is the temporal scope of the data used? | {
"A": "Single-scene imagery",
"B": "Short-term imagery ( ≤3 months)",
"C": "Mid-term imagery ( >3 and ≤12 months)",
"D": "Long-term imagery ( >1 year)",
"E": "All of above",
"F": "None of above"
} | C | {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "time span",
"term_explanation": "The total time span covered by the remote sensing imagery used in the study. This could be a single image, a short-term period (weeks to months), seasonal cycles, or long-term historical records... |
Earth Science - Remote Sensing | 2 | Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018 | Q5 | What type of remote sensing data is used? | {
"A": "Optical",
"B": "SAR",
"C": "LiDAR",
"D": "Multisource fusion",
"E": "All of above",
"F": "None of above"
} | A | {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "data type",
"term_explanation": "The primary data modality employed in the study. Categories include optical imagery, synthetic aperture radar (SAR), LiDAR, or combinations of multiple sources. The data type determines the spec... |
Earth Science - Remote Sensing | 2 | Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018 | Q6 | Which specific satellite data is used? | {
"A": "Sentinel-1",
"B": "Sentinel-2",
"C": "Luojia-1",
"D": "Sentinel-2 and Luojia-1",
"E": "All of above",
"F": "None of above"
} | D | {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "satellite",
"term_explanation": "The name(s) of satellite missions providing the source imagery, such as Sentinel-2, Landsat-8, Sentinel-1, MODIS, or commercial platforms like Planet or WorldView. This helps determine resolutio... |
Earth Science - Remote Sensing | 2 | Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018 | Q7 | What is the spatial resolution of the primary imagery used? | {
"A": "2 m",
"B": "10 m",
"C": "21 m",
"D": "27 m",
"E": "All of above",
"F": "None of above"
} | B | {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "spatial resolution",
"term_explanation": "The ground sampling distance (in meters) of the input imagery, indicating the size of the smallest detectable object. High-resolution imagery (e.g., <5 m) allows fine-scale mapping, whi... |
Earth Science - Remote Sensing | 2 | Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018 | Q8 | Are auxiliary features used beyond raw spectral bands? | {
"A": "Vegetation indices (e.g., NDVI)",
"B": "Water features (e.g., NDWI)",
"C": "Vegetation indices and Water indices",
"D": "Elevation / DEM",
"E": "All of above",
"F": "None of above"
} | D | {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "features",
"term_explanation": "Whether the study utilizes additional derived features such as vegetation indices (NDVI, LAI, FAPAR), land surface temperature, albedo, emissivity, topographic information (e.g., DEM), or other e... |
Earth Science - Remote Sensing | 2 | Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018 | Q9 | What type of model is implemented in this study? | {
"A": "SVM",
"B": "RF",
"C": "XGBoost",
"D": "CNN",
"E": "All of above",
"F": "None of above"
} | B | {
"Task-oriented Category": "Technical approach & details",
"question_key_term": "ML model",
"term_explanation": "The classification model used to perform land cover or land use mapping. Options include traditional machine learning algorithms (e.g., random forest, SVM), deep learning architectures (e.g., CNNs, U-... |
Earth Science - Remote Sensing | 2 | Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018 | Q10 | What performance metrics are reported? | {
"A": "Overall Accuracy (OA)",
"B": "F1-score",
"C": "Kappa",
"D": "IoU / mIoU",
"E": "All of above",
"F": "None of above"
} | A | {
"Task-oriented Category": "Technical approach & details",
"question_key_term": "performance metrics",
"term_explanation": "The quantitative indicators used to evaluate model performance, such as overall accuracy (OA), F1-score, Kappa coefficient, and mean intersection-over-union (mIoU). The selection of metrics... |
Earth Science - Remote Sensing | 2 | Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018 | Q11 | Is any comparative analysis included? | {
"A": "Compared with traditional classifiers (e.g., RF, SVM)",
"B": "Compared with deep models (e.g., U-Net variants)",
"C": "Ablation study",
"D": "No comparison reported",
"E": "All of above",
"F": "None of above"
} | D | {
"Task-oriented Category": "Conclusions & results",
"question_key_term": "comparative analysis",
"term_explanation": "Whether the study conducts comparisons with baseline models, traditional classifiers, or variants of the proposed method (e.g., ablation study, benchmark comparison). This provides insight into m... |
Earth Science - Remote Sensing | 2 | Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018 | Q12 | What is the reported overall accuracy (OA)? | {
"A": "40.6%",
"B": "57.5%",
"C": "61.2%",
"D": "64.1%",
"E": "All of above",
"F": "None of above"
} | A | {
"Task-oriented Category": "Conclusions & results",
"question_key_term": "overall accuracy",
"term_explanation": "The percentage of correctly classified samples over the total number of samples, as reported in the study. OA is a commonly used metric for remote sensing classification and provides a general measur... |
Earth Science - Remote Sensing | 3 | Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data | Q1 | What is the number of land-cover / land-use classes classified in this study? | {
"A": "2",
"B": "3",
"C": "9",
"D": "17",
"E": "All of above",
"F": "None of above"
} | C | {
"Task-oriented Category": "Study subject & experimental setup",
"question_key_term": "land cover",
"term_explanation": "The total number of land cover or land use categories defined for classification. This reflects the complexity of the classification task and determines how fine-grained the land surface repre... |
Earth Science - Remote Sensing | 3 | Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data | Q2 | What is the spatial extent of the study area? | {
"A": "6,229 km²",
"B": "100,000 km²",
"C": "250,000 km²",
"D": "656,889 km²",
"E": "All of above",
"F": "None of above"
} | F | {
"Task-oriented Category": "Study subject & experimental setup",
"question_key_term": "geographic area",
"term_explanation": "The geographic area covered by the study, typically measured in square kilometers. This affects the generalizability and computational complexity of the study and can range from local sit... |
Earth Science - Remote Sensing | 3 | Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data | Q3 | What is the geographic type of the study area? | {
"A": "Urban",
"B": "Rural",
"C": "Mixed",
"D": "Natural (e.g., forest, wetland, desert)",
"E": "All of above",
"F": "None of above"
} | C | {
"Task-oriented Category": "Study subject & experimental setup",
"question_key_term": "geographic type",
"term_explanation": "The geographic type or functional nature of the study region. Common categories include urban, rural, natural ecosystems (e.g., forest, wetland), or mixed types. The geographic context i... |
Earth Science - Remote Sensing | 3 | Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data | Q4 | What is the temporal scope of the data used? | {
"A": "Single-scene imagery",
"B": "Short-term imagery ( ≤3 months)",
"C": "Mid-term imagery ( >3 and ≤12 months)",
"D": "Long-term imagery ( >1 year)",
"E": "All of above",
"F": "None of above"
} | D | {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "time span",
"term_explanation": "The total time span covered by the remote sensing imagery used in the study. This could be a single image, a short-term period (weeks to months), seasonal cycles, or long-term historical records... |
Earth Science - Remote Sensing | 3 | Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data | Q5 | What type of remote sensing data is used? | {
"A": "Optical",
"B": "SAR",
"C": "LiDAR",
"D": "Multisource fusion",
"E": "All of above",
"F": "None of above"
} | A | {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "data type",
"term_explanation": "The primary data modality employed in the study. Categories include optical imagery, synthetic aperture radar (SAR), LiDAR, or combinations of multiple sources. The data type determines the spec... |
Earth Science - Remote Sensing | 3 | Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data | Q6 | Which specific satellite data is used? | {
"A": "Sentinel-1",
"B": "Landsat series",
"C": "Sentinel-2",
"D": "PlanetScope",
"E": "All of above",
"F": "None of above"
} | B | {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "satellite",
"term_explanation": "The name(s) of satellite missions providing the source imagery, such as Sentinel-2, Landsat-8, Sentinel-1, MODIS, or commercial platforms like Planet or WorldView. This helps determine resolutio... |
Earth Science - Remote Sensing | 3 | Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data | Q7 | What is the spatial resolution of the primary imagery used? | {
"A": "10 m",
"B": "18 m",
"C": "30 m",
"D": "60 m",
"E": "All of above",
"F": "None of above"
} | C | {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "spatial resolution",
"term_explanation": "The ground sampling distance (in meters) of the input imagery, indicating the size of the smallest detectable object. High-resolution imagery (e.g., <5 m) allows fine-scale mapping, whi... |
Earth Science - Remote Sensing | 3 | Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data | Q8 | Are auxiliary features used beyond raw spectral bands? | {
"A": "Vegetation indices (e.g., EVI)",
"B": "Water features (e.g., NDWI)",
"C": "Vegetation indices and Water indices",
"D": "Elevation / DEM",
"E": "All of above",
"F": "None of above"
} | A | {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "features",
"term_explanation": "Whether the study utilizes additional derived features such as vegetation indices (NDVI, LAI, FAPAR), land surface temperature, albedo, emissivity, topographic information (e.g., DEM), or other e... |
Earth Science - Remote Sensing | 3 | Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data | Q9 | What type of model is implemented in this study? | {
"A": "SVM",
"B": "RF",
"C": "J4.8 Classifier",
"D": "MLC",
"E": "All of above",
"F": "None of above"
} | E | {
"Task-oriented Category": "Technical approach & details",
"question_key_term": "ML model",
"term_explanation": "The classification model used to perform land cover or land use mapping. Options include traditional machine learning algorithms (e.g., random forest, SVM), deep learning architectures (e.g., CNNs, U-... |
Earth Science - Remote Sensing | 3 | Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data | Q10 | What performance metrics are reported? | {
"A": "Overall Accuracy (OA)",
"B": "F1-score",
"C": "Kappa",
"D": "OA and Kappa",
"E": "All of above",
"F": "None of above"
} | A | {
"Task-oriented Category": "Technical approach & details",
"question_key_term": "performance metrics",
"term_explanation": "The quantitative indicators used to evaluate model performance, such as overall accuracy (OA), F1-score, Kappa coefficient, and mean intersection-over-union (mIoU). The selection of metrics... |
Earth Science - Remote Sensing | 3 | Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data | Q11 | Is any comparative analysis included? | {
"A": "Compared with traditional classifiers (e.g., RF, SVM)",
"B": "Compared with deep models (e.g., U-Net variants)",
"C": "Ablation study",
"D": "No comparison reported",
"E": "All of above",
"F": "None of above"
} | A | {
"Task-oriented Category": "Conclusions & results",
"question_key_term": "comparative analysis",
"term_explanation": "Whether the study conducts comparisons with baseline models, traditional classifiers, or variants of the proposed method (e.g., ablation study, benchmark comparison). This provides insight into m... |
Earth Science - Remote Sensing | 3 | Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data | Q12 | What is the reported overall accuracy (OA)? | {
"A": "53.88%",
"B": "57.88%",
"C": "59.83%",
"D": "64.89%",
"E": "All of above",
"F": "None of above"
} | D | {
"Task-oriented Category": "Conclusions & results",
"question_key_term": "overall accuracy",
"term_explanation": "The percentage of correctly classified samples over the total number of samples, as reported in the study. OA is a commonly used metric for remote sensing classification and provides a general measur... |
Earth Science - Remote Sensing | 4 | Annual dynamics of global land cover and its long-term changes from 1982 to 2015 | Q1 | What is the number of land-cover / land-use classes classified in this study? | {
"A": "1",
"B": "7",
"C": "11",
"D": "20",
"E": "All of above",
"F": "None of above"
} | B | {
"Task-oriented Category": "Study subject & experimental setup",
"question_key_term": "land cover",
"term_explanation": "The total number of land cover or land use categories defined for classification. This reflects the complexity of the classification task and determines how fine-grained the land surface repre... |
Earth Science - Remote Sensing | 4 | Annual dynamics of global land cover and its long-term changes from 1982 to 2015 | Q2 | What is the spatial extent of the study area? | {
"A": "67,000 km²",
"B": "132,000 km²",
"C": "151,942 km²",
"D": "315,000 km²",
"E": "All of above",
"F": "None of above"
} | F | {
"Task-oriented Category": "Study subject & experimental setup",
"question_key_term": "geographic area",
"term_explanation": "The geographic area covered by the study, typically measured in square kilometers. This affects the generalizability and computational complexity of the study and can range from local sit... |
Earth Science - Remote Sensing | 4 | Annual dynamics of global land cover and its long-term changes from 1982 to 2015 | Q3 | What is the geographic type of the study area? | {
"A": "Urban",
"B": "Rural",
"C": "Mixed",
"D": "Natural (e.g., forest, wetland, desert)",
"E": "All of above",
"F": "None of above"
} | C | {
"Task-oriented Category": "Study subject & experimental setup",
"question_key_term": "geographic type",
"term_explanation": "The geographic type or functional nature of the study region. Common categories include urban, rural, natural ecosystems (e.g., forest, wetland), or mixed types. The geographic context i... |
Earth Science - Remote Sensing | 4 | Annual dynamics of global land cover and its long-term changes from 1982 to 2015 | Q4 | What is the temporal scope of the data used? | {
"A": "Single-scene imagery",
"B": "Short-term imagery ( ≤3 months)",
"C": "Mid-term imagery ( >3 and ≤12 months)",
"D": "Long-term imagery ( >1 year)",
"E": "All of above",
"F": "None of above"
} | D | {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "time span",
"term_explanation": "The total time span covered by the remote sensing imagery used in the study. This could be a single image, a short-term period (weeks to months), seasonal cycles, or long-term historical records... |
Earth Science - Remote Sensing | 4 | Annual dynamics of global land cover and its long-term changes from 1982 to 2015 | Q5 | What type of remote sensing data is used? | {
"A": "Optical",
"B": "SAR",
"C": "LiDAR",
"D": "Multisource fusion",
"E": "All of above",
"F": "None of above"
} | D | {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "data type",
"term_explanation": "The primary data modality employed in the study. Categories include optical imagery, synthetic aperture radar (SAR), LiDAR, or combinations of multiple sources. The data type determines the spec... |
Earth Science - Remote Sensing | 4 | Annual dynamics of global land cover and its long-term changes from 1982 to 2015 | Q6 | Which specific satellite data is used? | {
"A": "Sentinel-1",
"B": "Sentinel-2",
"C": "Luojia-1",
"D": "Multisources",
"E": "All of above",
"F": "None of above"
} | D | {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "satellite",
"term_explanation": "The name(s) of satellite missions providing the source imagery, such as Sentinel-2, Landsat-8, Sentinel-1, MODIS, or commercial platforms like Planet or WorldView. This helps determine resolutio... |
Earth Science - Remote Sensing | 4 | Annual dynamics of global land cover and its long-term changes from 1982 to 2015 | Q7 | What is the spatial resolution of the primary imagery used? | {
"A": "5 m",
"B": "10 m",
"C": "30 m",
"D": "5 km",
"E": "All of above",
"F": "None of above"
} | E | {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "spatial resolution",
"term_explanation": "The ground sampling distance (in meters) of the input imagery, indicating the size of the smallest detectable object. High-resolution imagery (e.g., <5 m) allows fine-scale mapping, whi... |
Earth Science - Remote Sensing | 4 | Annual dynamics of global land cover and its long-term changes from 1982 to 2015 | Q8 | Are auxiliary features used beyond raw spectral bands? | {
"A": "Vegetation indices only (e.g., NDVI, LAI, FAPAR)",
"B": "Vegetation + energy fluxes (e.g., ET, GPP)",
"C": "Vegetation + albedo/emissivity (e.g., BBE, white-sky albedo)",
"D": "Albedo/emissivity",
"E": "All of above",
"F": "None of above"
} | C | {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "features",
"term_explanation": "Whether the study utilizes additional derived features such as vegetation indices (NDVI, LAI, FAPAR), land surface temperature, albedo, emissivity, topographic information (e.g., DEM), or other e... |
Earth Science - Remote Sensing | 4 | Annual dynamics of global land cover and its long-term changes from 1982 to 2015 | Q9 | What type of model is implemented in this study? | {
"A": "SVM",
"B": "RF",
"C": "XGBoost",
"D": "CNN",
"E": "All of above",
"F": "None of above"
} | B | {
"Task-oriented Category": "Technical approach & details",
"question_key_term": "ML model",
"term_explanation": "The classification model used to perform land cover or land use mapping. Options include traditional machine learning algorithms (e.g., random forest, SVM), deep learning architectures (e.g., CNNs, U-... |
Earth Science - Remote Sensing | 4 | Annual dynamics of global land cover and its long-term changes from 1982 to 2015 | Q10 | What performance metrics are reported? | {
"A": "Overall Accuracy (OA)",
"B": "F1-score",
"C": "Kappa",
"D": "OA and Kappa",
"E": "All of above",
"F": "None of above"
} | A | {
"Task-oriented Category": "Technical approach & details",
"question_key_term": "performance metrics",
"term_explanation": "The quantitative indicators used to evaluate model performance, such as overall accuracy (OA), F1-score, Kappa coefficient, and mean intersection-over-union (mIoU). The selection of metrics... |
Earth Science - Remote Sensing | 4 | Annual dynamics of global land cover and its long-term changes from 1982 to 2015 | Q11 | Is any comparative analysis included? | {
"A": "Compared with traditional classifiers (e.g., RF, SVM)",
"B": "Compared with deep models (e.g., U-Net variants)",
"C": "Compared with previous products",
"D": "No comparison reported",
"E": "All of above",
"F": "None of above"
} | C | {
"Task-oriented Category": "Conclusions & results",
"question_key_term": "comparative analysis",
"term_explanation": "Whether the study conducts comparisons with baseline models, traditional classifiers, or variants of the proposed method (e.g., ablation study, benchmark comparison). This provides insight into m... |
Earth Science - Remote Sensing | 4 | Annual dynamics of global land cover and its long-term changes from 1982 to 2015 | Q12 | What is the reported overall accuracy (OA)? | {
"A": "73.54%",
"B": "86.51%",
"C": "87.12%",
"D": "92.26%",
"E": "All of above",
"F": "None of above"
} | B | {
"Task-oriented Category": "Conclusions & results",
"question_key_term": "overall accuracy",
"term_explanation": "The percentage of correctly classified samples over the total number of samples, as reported in the study. OA is a commonly used metric for remote sensing classification and provides a general measur... |
Earth Science - Remote Sensing | 5 | Annual maps of global artificial impervious area (GAIA) between 1985 and 2018 | Q1 | What is the number of land-cover / land-use classes classified in this study? | {
"A": "1",
"B": "3",
"C": "34",
"D": "155",
"E": "All of above",
"F": "None of above"
} | A | {
"Task-oriented Category": "Study subject & experimental setup",
"question_key_term": "land cover",
"term_explanation": "The total number of land cover or land use categories defined for classification. This reflects the complexity of the classification task and determines how fine-grained the land surface repre... |
Earth Science - Remote Sensing | 5 | Annual maps of global artificial impervious area (GAIA) between 1985 and 2018 | Q2 | What is the spatial extent of the study area? | {
"A": "108,962 km²",
"B": "340,625 km²",
"C": "218,859 km²",
"D": "797,076 km²",
"E": "All of above",
"F": "None of above"
} | D | {
"Task-oriented Category": "Study subject & experimental setup",
"question_key_term": "geographic area",
"term_explanation": "The geographic area covered by the study, typically measured in square kilometers. This affects the generalizability and computational complexity of the study and can range from local sit... |
Earth Science - Remote Sensing | 5 | Annual maps of global artificial impervious area (GAIA) between 1985 and 2018 | Q3 | What is the geographic type of the study area? | {
"A": "Urban",
"B": "Rural",
"C": "Mixed",
"D": "Natural (e.g., forest, wetland, desert)",
"E": "All of above",
"F": "None of above"
} | A | {
"Task-oriented Category": "Study subject & experimental setup",
"question_key_term": "geographic type",
"term_explanation": "The geographic type or functional nature of the study region. Common categories include urban, rural, natural ecosystems (e.g., forest, wetland), or mixed types. The geographic context i... |
Earth Science - Remote Sensing | 5 | Annual maps of global artificial impervious area (GAIA) between 1985 and 2018 | Q4 | What is the temporal scope of the data used? | {
"A": "Single-scene imagery",
"B": "Short-term imagery ( ≤3 months)",
"C": "Mid-term imagery ( >3 and ≤12 months)",
"D": "Long-term imagery ( >1 year)",
"E": "All of above",
"F": "None of above"
} | D | {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "time span",
"term_explanation": "The total time span covered by the remote sensing imagery used in the study. This could be a single image, a short-term period (weeks to months), seasonal cycles, or long-term historical records... |
Earth Science - Remote Sensing | 5 | Annual maps of global artificial impervious area (GAIA) between 1985 and 2018 | Q5 | What type of remote sensing data is used? | {
"A": "Optical",
"B": "SAR",
"C": "LiDAR",
"D": "Multisource fusion",
"E": "All of above",
"F": "None of above"
} | D | {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "data type",
"term_explanation": "The primary data modality employed in the study. Categories include optical imagery, synthetic aperture radar (SAR), LiDAR, or combinations of multiple sources. The data type determines the spec... |
Earth Science - Remote Sensing | 5 | Annual maps of global artificial impervious area (GAIA) between 1985 and 2018 | Q6 | Which specific satellite data is used? | {
"A": "Sentinel-1",
"B": "Landsat series",
"C": "VIIRS NTL",
"D": "Landsat series, Sentinel-1 and VIIRS NTL",
"E": "All of above",
"F": "None of above"
} | D | {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "satellite",
"term_explanation": "The name(s) of satellite missions providing the source imagery, such as Sentinel-2, Landsat-8, Sentinel-1, MODIS, or commercial platforms like Planet or WorldView. This helps determine resolutio... |
Earth Science - Remote Sensing | 5 | Annual maps of global artificial impervious area (GAIA) between 1985 and 2018 | Q7 | What is the spatial resolution of the primary imagery used? | {
"A": "10 m",
"B": "30 m",
"C": "100 m",
"D": "250 m",
"E": "All of above",
"F": "None of above"
} | B | {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "spatial resolution",
"term_explanation": "The ground sampling distance (in meters) of the input imagery, indicating the size of the smallest detectable object. High-resolution imagery (e.g., <5 m) allows fine-scale mapping, whi... |
Earth Science - Remote Sensing | 5 | Annual maps of global artificial impervious area (GAIA) between 1985 and 2018 | Q8 | Are auxiliary features used beyond raw spectral bands? | {
"A": "Vegetation indices (e.g., EVI)",
"B": "Vegetation + energy fluxes (e.g., ET, GPP)",
"C": "Water features (e.g., NDWI, MNDWI)",
"D": "Vegetation indices and Water indices",
"E": "All of above",
"F": "None of above"
} | C | {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "features",
"term_explanation": "Whether the study utilizes additional derived features such as vegetation indices (NDVI, LAI, FAPAR), land surface temperature, albedo, emissivity, topographic information (e.g., DEM), or other e... |
Earth Science - Remote Sensing | 5 | Annual maps of global artificial impervious area (GAIA) between 1985 and 2018 | Q9 | What type of model is implemented in this study? | {
"A": "Spatially Explicit",
"B": "Temporal Consistency",
"C": "Spatially Explicit and Temporal Consistency",
"D": "Transformer",
"E": "All of above",
"F": "None of above"
} | C | {
"Task-oriented Category": "Technical approach & details",
"question_key_term": "ML model",
"term_explanation": "The classification model used to perform land cover or land use mapping. Options include traditional machine learning algorithms (e.g., random forest, SVM), deep learning architectures (e.g., CNNs, U-... |
Earth Science - Remote Sensing | 5 | Annual maps of global artificial impervious area (GAIA) between 1985 and 2018 | Q10 | What performance metrics are reported? | {
"A": "Overall Accuracy (OA)",
"B": "F1-score",
"C": "Kappa",
"D": "OA and Kappa",
"E": "All of above",
"F": "None of above"
} | A | {
"Task-oriented Category": "Technical approach & details",
"question_key_term": "performance metrics",
"term_explanation": "The quantitative indicators used to evaluate model performance, such as overall accuracy (OA), F1-score, Kappa coefficient, and mean intersection-over-union (mIoU). The selection of metrics... |
Earth Science - Remote Sensing | 5 | Annual maps of global artificial impervious area (GAIA) between 1985 and 2018 | Q11 | Is any comparative analysis included? | {
"A": "Compared with traditional classifiers (e.g., RF, SVM)",
"B": "Compared with deep models (e.g., U-Net variants)",
"C": "Compared with previous products",
"D": "No comparison reported",
"E": "All of above",
"F": "None of above"
} | C | {
"Task-oriented Category": "Conclusions & results",
"question_key_term": "comparative analysis",
"term_explanation": "Whether the study conducts comparisons with baseline models, traditional classifiers, or variants of the proposed method (e.g., ablation study, benchmark comparison). This provides insight into m... |
Earth Science - Remote Sensing | 5 | Annual maps of global artificial impervious area (GAIA) between 1985 and 2018 | Q12 | What is the reported overall accuracy (OA)? | {
"A": "15%",
"B": "43%",
"C": "70%",
"D": "89%",
"E": "All of above",
"F": "None of above"
} | D | {
"Task-oriented Category": "Conclusions & results",
"question_key_term": "overall accuracy",
"term_explanation": "The percentage of correctly classified samples over the total number of samples, as reported in the study. OA is a commonly used metric for remote sensing classification and provides a general measur... |
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