Sea ice leads are produced from deformational forces, which break apart the ice surface and expose open water areas or leads. Leads are the primary regulators of heat in Arctic sea ice during the polar night. Partially-frozen and re-frozen leads produce smaller heat exchanges than open leads due to the absence of a warm water surface. Thus detecting leads is important because they may be used as an indirect way of estimating air-sea heat fluxes. To quantify winter-time leads, we utilize Sentinel-1 C-Band Synthetic Aperture Radar (SAR) data to examine sea ice images through heavy cloud cover. We employ a support vector machine learning technique in a cloud computation environment (Google Earth Engine) to detect and quantify lead areas. With the use of dual-polarization data, we improve the separation of leads from other elongated features (e.g., ridges) in the Sentinel-1 dataset by adding altimetry information from ICESat-2. In addition to typical texture analysis to assess surface roughness, the ICESat-2 ATL-10 data allows us to train the algorithm by discretizing leads and ridges by their freeboard values. Performing this method in a cloud environment allows processing of a large volume of satellite data and converting it into a time series of leads properties. Overall, our method improves lead detection with dual-polarization SAR data while simultaneously providing a big data solution for SAR image processing. The interannual variability of leads and newly formed ice fractions were found for the winters of 2017-2020. Finally, we compare the results from previous studies to validate our cloud-derived sea ice lead detection maps.