For the Mineral Wells study, a supervised machine learning algorithm was used to create an initial, first-pass impervious surface dataset. Ar- eas representative of land cover classes were manually defined from aerial imagery. Seven different land cover classes were used in this first pass. All seven land cover classes were eventually reclassified as either pervious or impervious surface coverage.
The three additional machine learning datasets used to improve classification quality. Photo: LAN
The resultant image exhibited unresolved areas where the algorithm was unable to definitively discriminate between land cover classes. The classified dataset also exhibited notable areas of false positives on impervious surface coverage. This initial, first-pass impervious surface dataset provided a near-adequate level of resolution and was temporar- ily set aside while an improved dataset was developed. To address both unresolved areas and areas of false positives, three additional machine learning training datasets were made to better dis- criminate between spectrally similar land cover classes. Each training set consisted of two of the initial seven land cover classes and a third class comprised of all remaining land cover classes to provide a con- trasting backdrop against the classes of interest. By limiting the number of classes of interest in each training dataset (two rather than seven) the algorithms that create the statistical de- scriptions of land cover classes are able to do so in relative isolation, without the interfering influence of other land cover classes. For ex- ample, it can be difficult to discriminate between dirt and pavement. Both land cover classes can appear grey or brownish to the eye and have relatively high infrared reflectivity. In the full, seven-class train- ing set, the machine learning algorithm must successfully discriminate between dirt and pavement while simultaneously discriminating be- tween five other land cover classes. By reducing the number of classes, the algorithm is better able to discriminate dirt from pavement because there is no need to separately identify other land cover classes.
Compared to the initial, first-pass classification dataset, the composite classification reduces the number of false positives on impervious sur- face at the expense of additional areas of uncertainty. The composite classification, which reflected ground conditions more accurately, formed the basis of the ultimate impervious surface dataset. Values from the initial, first-pass dataset were used to fill gaps in areas of uncertainty, producing a product that capitalized on the best parts of both products. Smoothing algorithms and building footprints were applied to the impervious dataset to further refine the product. Data Integration Access to County Appraisal District parcel data allowed the tabulation of impervious surface data at the parcel level. This data helped deter- mine how much impervious surface each parcel contained. Integrating parcel-level impervious surface dataset into the existing utility billing scheme required careful execution of automated process- es. The two datasets were large enough that manual integration was unpractical. Consequently, automated integration methods were developed. The addresses were the only common identifier between the two data sets. Unfortunately, there wasn’t a perfect match between utility billing addresses and parcel addresses. For example, 123 N Example Street and 123 N Example St are not perfect matches. While most human operators would probably recognize that these two addresses refer to the same property, a computer looking for a perfect, one-for-one match would reject the pairing. An address similarity tool was created to address and resolve this con- flict. In both the billing and parcel datasets, addresses were separated into their constituent components. An address standardizing tool was used to ensure that any direction or street suffix conflicts were resolved.
The resultant classifications from these three training datasets were combined into a single composite dataset. The composite dataset was reclassified to reflect either pervious or impervious surface coverage.
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