Once there I cannot change the layer's symbology from the black and white 1-11 color scale to indicate the 11 distinct land-use classes. I have tried applying symbology from layer (I found a layer with the category names) which changes the category names but then no colors appear on the map.
I really look forward to the results of these efforts. For if the effective resolution is as low as you say (in my experience it seems a little better than that), I suppose robust land cover mapping based on multi-temporal S-2 data is currently not possible at 10m spatial resolution.
Sentinel-2 10m Land Use Land Cover Time Series Download
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Do you think resampling the time series to 20 or 30 m (perhaps applying bilinear interpolation to retain gradient info as much as possible) prior to performing a supervised classification would produce conceptually better results?
Regarding your second question, sorry, I am not an expert of land cover classification, I do not know what is best, resampling before classification or after. If you plan to use texture or contextual information as an input feature, it is still probably better to start working at 10m.
The general aim of this work was to elaborate efficient and reliable aggregation method that could be used for creating a land cover map at a global scale from multitemporal satellite imagery. The study described in this paper presents methods for combining results of land cover/land use classifications performed on single-date Sentinel-2 images acquired at different time periods. For that purpose different aggregation methods were proposed and tested on study sites spread on different continents. The initial classifications were performed with Random Forest classifier on individual Sentinel-2 images from a time series. In the following step the resulting land cover maps were aggregated pixel by pixel using three different combinations of information on the number of occurrences of a certain land cover class within a time series and the posterior probability of particular classes resulting from the Random Forest classification. From the proposed methods two are shown superior and in most cases were able to reach or outperform the accuracy of the best individual classifications of single-date images. Moreover, the aggregations results are very stable when used on data with varying cloudiness. They also enable to reduce considerably the number of cloudy pixels in the resulting land cover map what is significant advantage for mapping areas with frequent cloud coverage.
Our modeling approach relies on semi-supervised deep learning and requires spatially dense (i.e., ideally wall-to-wall) annotations. To collect a diverse set of training and evaluation data, we divided the world into three regions: the Western Hemisphere (160W to 20W), Eastern Hemisphere-1 (20W to 100E), and Eastern Hemisphere-2 (100E to 160W). We further divided each region by the 14 RESOLVE Ecoregions biomes22. We collected a stratified sample of sites for each biome per region based on NASA MCD12Q1 land cover for 20174. Given the availability of higher-resolution LULC maps in the United States and Brazil, we used the NLCD 201610 and MapBiomas 201720 LULC products respectively in place of MODIS products for stratification in these two countries.
We finally perform a series of augmentations (random rotation and random per-band contrasting) to our input data to improve generalizability and performance of our model. These augmentations are applied four times to each example to yield our final training dataset of examples paired with class distributions, masks, and weights (Fig. 3).
Near-Real-Time (NRT) prediction workflow. Input imagery is normalized following the same protocol used in training and the trained model is applied to generate land cover predictions. Predicted results are masked to remove cloud and cloud shadow artifacts using Sentinel-2 cloud probabilities (S2C), the Cloud Displacement Index (CDI) and a directional distance transform (DDT), then added to the Dynamic World image collection.
We find single-date Dynamic World classifications agree with the annotators nearly as well as the annotators agree amongst each other. The Dynamic World NRT product also achieves performance near, or exceeding many popular regional and global annual LULC products when compared to annotations for the same validation tiles. However, we have observed that performance varies spatially and temporally as a function of both the quality of S2 cloud masking and variability in land cover and condition.
Dynamic World tends to perform most strongly in temperate and tree-dominated biomes. Arid shrublands and rangelands were observed to present the greatest source of confusion specifically between crops and shrub. In Fig. 10, we demonstrate this phenomenon by observing that the maximum of estimated probabilities between crops and shrubs tends towards 0.5 in a sample of arid shrubland in Texas (seen by the low contrast purple coloring) even though this region does not contain cultivated land. By visual qualitative inspection, Dynamic World identifies grasslands better than the generally low agreement suggested by our Expert Consensus (30.1% for Dynamic World to 50% by non-experts, a 19.9% delta), and identifies crops more poorly than the generally high agreement suggested by our Expert consensus (88.9% by Dynamic World to 93.7% by non-experts, a 4.8% delta).
Demonstration of relative weakness exhibited in Dynamic World in separating arid shrubland from crops. (a) An oil field in Texas, USA; (b) Agricultural mosaic in Florida, USA. High resolution image shown for reference. Estimated class prediction probabilities scaled from [0, 1] with red corresponding to the maximum probability of the crops class and blue corresponding to the maximum probability of the shrub & scrub class. In arid shrubland, the estimated probabilities for shrub and crops are more similar (purple) than in temperate or other biomes. The probabilities were averaged per-pixel over July 2021 and the reference imagery was taken from the Google Maps Satellite layer.
We thank Tyler A. Erickson at Google for assistance with the previous version of our dataset explorer app. We thank Matt Hansen and the University of Maryland Global Land Analysis and Discovery lab for contributions to external validation. Development of the Dynamic World training data was funded in part by the Gordon and Betty Moore Foundation.
C.F.B. was the primary author and developed and implemented the modeling, cloud masking, and inference methods. S.P.B., S.B.H., J.M., and A.M.T. oversaw the training data collection and technical validation. B.G-W., M.W., F.S., and C.H. assisted with training data collection, developed the taxonomy and use-case guidance, and supported the additional validation. T.B., W.C., R.H., S.I., K.S., O.G., and R.M. supported and contributed to the inference and data pipelines used in production of Dynamic World. V.J.P. contributed writing, the time-series explorer app, and Top-1 probability hillshade visualization.
The use of deep learning (DL) approaches for the analysis of remote sensing (RS) data is rapidly increasing. DL techniques have provided excellent results in applications ranging from parameter estimation to image classification and anomaly detection. Although the vast majority of studies report precision indicators, there is a lack of studies dealing with the interpretability of the predictions. This shortcoming hampers a wider adoption of DL approaches by a wider users community, as model's decisions are not accountable. In applications that involve the management of public budgets or policy compliance, a better interpretability of predictions is strictly required. This work aims to deepen the understanding of a recurrent neural network for land use classification based on Sentinel-2 time series in the context of the European Common Agricultural Policy (CAP). This permits to address the relevance of predictors in the classification process leading to an improved understanding of the behaviour of the network. The conducted analysis demonstrates that the red and near infrared Sentinel-2 bands convey the most useful information. With respect to the temporal information, the features derived from summer acquisitions were the most influential. These results contribute to the understanding of models used for decision making in the CAP to accomplish the European Green Deal (EGD) designed in order to counteract climate change, to protect biodiversity and ecosystems, and to ensure a fair economic return for farmers.
Time series of annual global maps of land use and land cover (LULC). It currently has data from 2017-2021. The maps are derived from ESA Sentinel-2 imagery at 10m resolution. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year. This dataset was generated by Impact Observatory, who used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. The global map was produced by applying this model to the Sentinel-2 annual scene collections on the Planetary Computer. Each of the maps has an assessed average accuracy of over 75%. These datasets produced by Impact Observatory and licensed by Esri were fetched from Microsoft Planetary Computer's data catalog & storage
This map uses an updated model from the 10-class model and combines Grass(formerly class 3) and Scrub (formerly class 6) into a single Rangeland class (class 11). The original Esri 2020 Land Cover collection uses 10 classes (Grass and Scrub separate) and an older version of the underlying deep learning model. The Esri 2020 Land Cover map was also produced by Impact Observatory and you can find it in GEE here. The map remains available for use in existing applications. New applications should use the updated version of 2020 once it is available in this collection, especially when using data from multiple years of this time series, to ensure consistent classification. e24fc04721
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