# Interactive images Each figure of the journeys on a page of its own, with every control and no story. ## Building data cubes from image collections - [Period and cell of a regular cube](/interactive-images/regular-cubes-period-resolution.md): The grid a regular cube agrees on: the tiles in their projection, the region as cells, one cell over the pixels of the bands, and the intervals of the period. [From step 3 of Building data cubes from image collections](/regular-cubes.md). In the sits book: [Building regular data cubes](https://e-sensing.github.io/sitsbook/dc_regularize.html). - [Where each cell of a regular image came from](/interactive-images/regular-cubes-provenance.md): A year of one tile, period by period: the images in sits' order, their masked cells, the fill image by image, and the image each cell of the regular image came from. [From step 4 of Building data cubes from image collections](/regular-cubes.md). In the sits book: [Building regular data cubes](https://e-sensing.github.io/sitsbook/dc_regularize.html). - [A regular cube, by date and by place](/interactive-images/regular-cubes-cube.md): A year of one tile made regular by sits: a stack of one image per period, the series of one cell in every band, the properties of a regular cube, and the dates where cells have no value. [From step 5 of Building data cubes from image collections](/regular-cubes.md). In the sits book: [Earth observation data cubes](https://e-sensing.github.io/sitsbook/datacubes.html) and [Building regular data cubes](https://e-sensing.github.io/sitsbook/dc_regularize.html). ## From series to embeddings - [A pixel through time](/interactive-images/series.md): The window month by month, and each band of one pixel as a time series, with the months sits filled in. [From step 1 of From series to embeddings](/embeddings.md). In the sits book: [Satellite image time series](https://e-sensing.github.io/sitsbook/timeseries.html) and [Foundational Models and Embeddings](https://e-sensing.github.io/sitsbook/embeddings.html). - [From a series to a vector](/interactive-images/shape.md): The raw series of one pixel as a table, with the months sits filled in, and the same pixel as the values its encoder writes. [From step 2 of From series to embeddings](/embeddings.md). In the sits book: [Foundational Models and Embeddings](https://e-sensing.github.io/sitsbook/embeddings.html) and [Building regional embeddings: a full example](https://e-sensing.github.io/sitsbook/emb_example.html). - [Pre-training, replayed](/interactive-images/pretraining.md): The recorded loss of two real pre-training runs, and labelled samples at recorded epochs of the smaller one beside the share whose neighbours share their label. [From step 4 of From series to embeddings](/embeddings.md). In the sits book: [Building regional embeddings: a full example](https://e-sensing.github.io/sitsbook/emb_example.html) and [Embedding Methods in SITS](https://e-sensing.github.io/sitsbook/emb_methods.html). - [One embedding, band by band](/interactive-images/vector.md): The values of one pixel's embedding as bars, each band as a map, and the window drawn from any three bands. [From step 5 of From series to embeddings](/embeddings.md). In the sits book: [Foundational Models and Embeddings](https://e-sensing.github.io/sitsbook/embeddings.html) and [Building regional embeddings: a full example](https://e-sensing.github.io/sitsbook/emb_example.html). ## From a cube to labelled samples - [Patterns of labelled samples](/interactive-images/time-series-patterns.md): The book's recorded MODIS samples of Mato Grosso, their class medians and quartiles, and the fitted sits_patterns() curves, aligned within each reference period. [From step 1 of From a cube to labelled samples](/time-series.md). In the sits book: [Basic operations on image time series](https://e-sensing.github.io/sitsbook/ts_basics.html). - [Any pixel of the Sinop cube, as a time series](/interactive-images/time-series-location.md): The recorded sits_get_data() output for every pixel of the sits book's Sinop MODIS window, a separate dataset from this journey's Cerrado point: any pixel's series, the values the record marks as filled, and the row sits makes of it. [From step 2 of From a cube to labelled samples](/time-series.md). In the sits book: [Basic operations on image time series](https://e-sensing.github.io/sitsbook/ts_basics.html). - [One labelled Sinop place, every year](/interactive-images/time-series-years.md): A place of the sits book's Sinop CSV in every agricultural year of a recorded MODIS run: each year's distance to the year its label holds for, against its label's threshold. A distance is a measure; no year gets a label. [From step 4 of From a cube to labelled samples](/time-series.md). In the sits book: [Basic operations on image time series](https://e-sensing.github.io/sitsbook/ts_basics.html) and [Machine learning algorithms for image time series](https://e-sensing.github.io/sitsbook/cl_machinelearning.html). ## From samples to a land use map - [A small network learning as you watch](/interactive-images/classifier.md): A small neural network trains in your browser on the time series or the embeddings of the labelled samples, as you choose, and the window's map is repainted after each epoch beside the loss. [From step 2 of From samples to a land use map](/classification.md). In the sits book: [Machine learning algorithms for image time series](https://e-sensing.github.io/sitsbook/cl_machinelearning.html) and [Building regional embeddings: a full example](https://e-sensing.github.io/sitsbook/emb_example.html). - [The land use map on the ground](/interactive-images/map.md): The land use map classified from the time series or the embeddings, as you choose: the probability of each class, the strongest class with and without smoothing, and a divider to compare the map with the image of the window and, on the embeddings, with the embeddings too. [From step 7 of From samples to a land use map](/classification.md). In the sits book: [Machine learning algorithms for image time series](https://e-sensing.github.io/sitsbook/cl_machinelearning.html) and [Building regional embeddings: a full example](https://e-sensing.github.io/sitsbook/emb_example.html). ## From time series to a map of temporal variability - [Two cells, which spreads more?](/interactive-images/temporal-variability-profile.md): Two cells of the study window with the same number of valid monthly values, their profiles on one axis. Guess which spreads more, then see each cell's temporal standard deviation. [From step 3 of From time series to a map of temporal variability](/temporal-variability.md). References: [R statistics, sd](https://stat.ethz.ch/R-manual/R-devel/library/stats/html/sd.html) and [stars, st_apply](https://r-spatial.github.io/stars/reference/st_apply.html). - [A map of temporal variability](/interactive-images/temporal-variability-map.md): The temporal standard deviation of every cell of the study window, computed with stars, beside the selected cell's monthly profile; a second view maps each cell's valid months, for each period. [From step 4 of From time series to a map of temporal variability](/temporal-variability.md). References: [stars, st_apply](https://r-spatial.github.io/stars/reference/st_apply.html), [Pebesma and Bivand, Spatial Data Science, chapter 6, Data Cubes](https://r-spatial.org/book/06-Cubes.html) and [Pinned cube snapshot, e-sensing/cerrado_cube_reg_2018](https://huggingface.co/datasets/e-sensing/cerrado_cube_reg_2018/tree/af1d4f67fee0fcaba35941f4ac5d7d9e87d4fa85).