Sky and cloud sequence datasets: fixed cameras, irradiance and temporal forecasting Evaluate sky and cloud sequences through cadence, camera geometry, sensor pairing, forecast targets, preparation costs and leakage-aware evaluation. Sky and cloud sequence datasets record changing atmospheric appearance from fixed cameras or repeated observations, sometimes paired with irradiance or other measurements. They can support cloud motion, segmentation, temporal forecasting and solar-energy research. A time-lapse sequence, a continuous video and a set of independent sky photos are different deliverables. Buyers should define cadence, camera geometry, timestamps, weather coverage, target measurements and evaluation periods. Fiund can assess a fixed-camera sky-data brief and potential sources, with availability, measurement provenance, permissions, preparation, sample review and commercial terms confirmed for the selected collection. Primary scope Temporal sky observations, not independent scenic photographs Core fields Timestamp, cadence, camera/calibration, quality and target measurement Public reference CloudCV: ten-second daytime observations over a documented period Evaluation priority Held-out days, weather regimes, seasons and camera sites A sky sequence is not defined by its file container A camera can save periodic JPEG frames, a timelapse video or a continuous stream. All may show changing clouds, but their timing and information differ. A model forecasting short-term irradiance needs a trustworthy observation clock and appropriately aligned targets. A model learning cloud appearance may use independent frames. Define whether the deliverable preserves actual sampling intervals or compresses time into playback. An MP4 label alone does not establish continuous capture or a uniform cadence. Fixed-camera geometry also matters. A fisheye all-sky imager, a narrow sky view and a conventional outdoor camera show different portions of the atmosphere. Buildings, trees, horizon obstructions, lens contamination and exposure control can affect the visible signal. State whether the task needs cloud segmentation, cloud motion, sky condition classification or forecasting of a measured quantity. Do not infer a physical weather measurement solely from an attractive image sequence. Public references and the role of paired measurements [CloudCV’s public laboratory release](https://data.openei.org/submissions/8294) describes sky images paired with irradiance observations every ten seconds during daylight over ninety days in 2019. It is a useful concrete reference for cadence and target pairing, not a statement of worldwide or all-season coverage. The dataset’s site, period, instruments and release conditions are important parts of a comparison. Its current availability should be checked at the source before relying on a download plan. [SKIPP’D’s original paper](https://arxiv.org/abs/2207.00913) describes a longer sky-image and photovoltaic-output research release. These resources show that imagery and measured targets can be paired, but the target type, sampling and environment differ. Irradiance, photovoltaic power and cloud labels are not interchangeable supervision. A buyer should specify which quantity is needed, its units, instrument provenance and alignment, rather than treating any solar-related column as the same forecast target. Source form Potential use Limitation to check Independent sky photos Appearance classification/segmentation No reliable temporal progression Fixed-cadence image sequence Motion and short-horizon forecasting Gaps and timestamp quality Timelapse export Human review and condensed temporal patterns Playback time differs from capture time Continuous camera video Fine motion within a view Storage, exposure and actual frame fidelity Images plus sensor target Defined forecasting research Instrument, clock and target meaning Multi-site collection Transfer across conditions Site geometry and independent weather coverage Buyers and the technical question behind demand Potential users include solar-forecasting researchers, energy analytics developers, environmental-vision teams and weather-image model builders. These are application segments rather than verified Fiund customers. A forecast model may need a sequence of past observations and a future measured target. A segmentation model may instead need pixel-level cloud/sky masks on independent frames. The buyer should define horizon, input duration, prediction target and the error conditions that matter before choosing a source. Long runtime does not guarantee varied atmospheric conditions. A clear week can supply many nearly identical frames; a short unstable period can contain rapid transitions. Count independent days, weather regimes, seasons and camera sites alongside file volume. If the source includes only daylight or excludes poor-quality periods, report that scope. A model should not be described as handling nighttime, precipitation or unfamiliar sites unless those conditions are actually represented and evaluated. Timestamps and alignment are core data quality Record capture time, timezone, clock source, daylight-saving handling where relevant, and any uncertainty or drift. A file’s modification time may reflect copying rather than observation. If images and instruments use different clocks, preserve the mapping and alignment method. For a forecasting task, even a small offset can change whether an example uses past or future information. Define the allowed timing tolerance for the actual horizon rather than adopting a generic synchronization claim. Missing frames should be explicit. Interpolation or duplication can make a sequence appear smooth while changing the physical signal. If preprocessing fills gaps, keep a mask indicating which values are observed and which are derived. The same applies to target measurements. A model can learn an artifact of gap filling or quality filtering instead of the intended relationship. Preserve both raw references and standardized inputs so those choices can be audited and revised. A schema for temporal sky observations A useful manifest connects camera/site, original frame or video, capture time, cadence, quality, processing and target observations. Calibration can describe projection, field of view, horizon mask or other needed geometry, but should not be assumed if absent. For fisheye images, a rectangular pixel displacement is not necessarily a uniform angular motion. State the representation and transformations relevant to the task. If cloud masks are annotated, define treatment of haze, thin cloud, sun glare and uncertain boundaries. A single hard binary mask can conceal genuine ambiguity. For target labels derived from a model, preserve that provenance rather than describing them as instrument measurements. If the project combines sources, document units, sampling and normalization separately before merging. Similar column names do not establish physically equivalent values. Field group Define Acceptance check site/camera/session Independent capture location and equipment Site identity is stable across releases capture time/cadence Observation clock and intended spacing Gaps, duplicates and drift are visible projection/calibration Lens/view geometry and horizon treatment Pixel/angle interpretation is documented image quality Exposure, contamination, obstruction and missing state Exclusions match agreed rules target measurement Quantity, units, instrument and quality flag Target belongs to the correct observation period derivative mapping Crop, resize, timelapse or normalization Time is not changed silently source/use records Release and proposed permissions Access is distinct from redistribution scope Price and transparent preparation estimates No representative public average commercial license price for fixed-camera sky sequences is established here. Costs can include source access, camera deployment, maintenance, site permissions, instrument pairing, timestamp reconstruction, quality filtering, labels and delivery. A price per frame can be misleading because neighboring observations are highly correlated. Camera-days, usable observation periods, sites and paired records may be more useful quote units depending on the task. For a hypothetical workload, suppose a project selects forty-five camera-days and allocates an assumed twelve minutes per day for automated-quality review and visual spot checks. That is nine hours before investigating exceptions. If a further fifteen hours is budgeted for timestamp/target alignment and the chosen loaded rate is $40 per hour, the modeled preparation component is $960. These are stated planning assumptions, not observed rates or a Fiund quote. Capture, rights, scientific validation, annotation and delivery are not included. A collection with reliable pairing may be more valuable than a larger archive requiring clock reconstruction. Compare accepted observation windows, gap rates, target availability and independent sites beside cost. Ask whether a quote includes raw measurements, quality flags and processing code or only a finished timelapse. If new collection is proposed, define maintenance and the treatment of camera outages rather than counting every planned day as guaranteed usable data. Sample acceptance and a worked comparison One candidate archive may contain continuous-looking timelapse files with no raw timestamps. Another may provide periodic frames with explicit gaps and sensor records. The first can be useful for visual demonstration, while the second may better support scientific forecasting. Normalize the comparison to the model input and target rather than preferring a video container. A pilot should inspect complete days or representative windows, not only visually dramatic cloud scenes. Check timestamp monotonicity, actual cadence, repeated frames, exposure shifts, lens obstructions, target alignment and the stated quality policy. Include clear skies, changing cloud, sun glare and difficult conditions represented in the proposed release. Verify that the model input does not use future information through centered smoothing or a composite that extends beyond the prediction time. Technical file validity is a separate check from meaningful temporal pairing. Evaluation design and failure examples Keep overlapping windows from one observation period grouped appropriately. A random frame split can place nearly identical neighboring observations in training and evaluation. For forecasting, preserve chronological constraints and define whether the test concerns a later period, a different season or a new site. Repeated weather patterns do not make those tests equivalent. Report camera and site scope rather than claiming general environmental transfer from one fixed view. Common failures include upload times used as capture times, target columns shifted by a sampling interval, timelapse playback treated as physical cadence, and missing frames filled without flags. Another is training on a derived target that already incorporates information from the future test interval. A useful review identifies these mechanisms explicitly and distinguishes correctable preparation errors from unavailable source evidence. Unknown quality or target coverage should remain unknown rather than being counted as a successful observation. A concrete forecast-window audit Suppose a model receives several recent sky observations and predicts a later measured target. The source manifest must show which samples are genuinely earlier, how gaps are handled and whether any smoothing window includes future data. A visually smooth time series can conceal leakage if preparation uses observations beyond the prediction time. Check the preprocessing boundary, not only the apparent timestamp order. A pilot can reproduce a few input/target pairs from raw references and compare them with the delivered examples. Record target quality flags, omitted frames and the reason a window is accepted. If the source lacks reliable clocks, decide whether it remains useful for appearance or motion analysis rather than pretending it supports the original forecasting task. This preserves value while keeping the data claim within the evidence. Providers, deals and Fiund sourcing Potential routes include public laboratory releases under their terms, original camera archive holders, environmental research projects and commissioned capture where feasible. [LXT’s video collection service](https://www.lxt.ai/services/video-data-collection/) is one public commercial custom-capture route to discuss, not evidence that it has an existing paired sky/instrument collection. Source imagery and a validated scientific target may require different partners and agreements. No verified fixed-camera-sky-specific AI-training license deal amount is established by the reviewed sources. Solar project contracts, instrument purchases and forecasting-software subscriptions should not be represented as dataset unit prices. Send Fiund the task, camera geometry, cadence, target measurement, period/site needs, quality policy and intended uses. Fiund can assess potential source fit and review requirements, with availability, permissions, measurement provenance, preparation, acceptance and commercial terms confirmed for the selected collection. Is a timelapse equivalent to continuous video? No. Its playback timing can compress or alter the capture interval. Preserve original timestamps and cadence, and identify missing or duplicated observations. Can sky imagery alone supply measured irradiance? It can be used in a model, but a measured target requires an actual linked instrument record with units, timing and quality information. Keep inferred labels distinct from measurements. Why avoid random frame splits? Adjacent sky observations are strongly correlated, and overlapping windows may share most of their content. Choose chronological, site or seasonal holdouts that reflect the intended evaluation. Sources CloudCV 10-second sky images and irradiance dataset: https://data.openei.org/submissions/8294 SKIPP’D original sky/solar forecasting paper: https://arxiv.org/abs/2207.00913 LXT video collection service: https://www.lxt.ai/services/video-data-collection/