机器学习 深度学习 ai
The process of cartographic generalization is used to produce a harmonized picture at different scales of geospatial features.
吨他处理制图综合的用于产生在地理空间特征的不同尺度一个统一图片。
Generalization is an essential part of any cartographic production process and is, generally, a process that is still at least partly, manually driven. The move to ENC charting has enabled some degree of automation of chart creation at different scales through the development of features for managing “scale-dependent“ features.
泛化是任何制图生产过程中必不可少的部分，并且通常是至少部分仍由手动驱动的过程。 通过开发用于管理“与比例有关的”功能的功能，向ENC制图的转变使不同比例的图表创建具有一定程度的自动化。
Database driven production systems, able to store the data for multiple charts in a single database instance, are then able to reuse features for different charts reducing the need for manual intervention.
由数据库驱动的生产系统能够在一个数据库实例中存储多个图表的数据，然后能够为不同的图表重用功能，从而减少了人工干预的需求。
The issue remains though, that many features require extensive manual editing in order to produce generalized products which are acceptable to both cartographer and end-user.
但是，问题仍然存在，许多功能需要大量的手动编辑才能生成制图师和最终用户都可以接受的通用产品。
There is, therefore, a large potential to increase the efficiency of any data production system by automating generalization into the chart production process as far as possible.
因此，通过尽可能地将归纳自动化到图表生成过程中，有很大的潜力提高任何数据生成系统的效率。
From a generalization point of view, bathymetric content is probably the most challenging as it is both one of the most (if not, THE most) important and safety-critical elements of the navigational chart and also one of the most complex and subtle in the practice of marine cartography.
从一般的角度来看，测深内容可能是最具挑战性的内容，因为它既是导航图中最重要(如果不是，最重要)且对安全性至关重要的元素之一，也是导航图中最复杂，最微妙的元素之一。海洋制图的实践。
Bathymetric content is composed primarily of the following features and attributes:
测深内容主要由以下功能和属性组成：
Individual soundings along with attribution containing various quality parameters
包含不同质量参数的单个声音以及归因
Areas delimiting specified ranges of depths
划定指定深度范围的区域
Contours denoting lines of equal-depth (in practice these are the perimeters of the depth area features in the previous point)
等高线表示等深线(实际上，这是上一点中深度区域特征的周长)
Value of sounding attributes on individual features, rocks, wrecks, etc 各个要素，岩石，残骸等上的测深属性的价值The collection, processing, and compilation of bathymetry data are the most labor-intensive and safety-critical of all phases of marine cartography and the resultant surface of depth areas and sounding arrays forms the essential surface for navigation which is presented to the chart’s end user.
航海图数据的收集，处理和汇编是海洋制图各个阶段中劳动强度最大，最安全的关键 ，深度区域和测深阵列的结果表面构成了导航的基本表面，并向海图的最终用户呈现。
Bathymetric source data is obtained from the raw, dense, survey information gathered by sensors from survey vessels and aircraft. Raw survey data is cleaned, validated, and harmonized into a large, dense set of candidate source depths soundings.
测深源数据是从传感器，调查船和飞机收集的原始，密集的调查信息中获得的。 原始的调查数据将被清理，验证并统一为大量密集的候选源深度测深。
From these surfaces, contours and a large set of candidate spot soundings are derived. Features are selected from the available source and sub-processes such as thinning, critical sounding designation and deconfliction with existing sources all take place and are and used to compile the resultant chart, whether by new edition (replacement) or update. From a generalization point of view, the selection of “appropriate” depth vectors from the available source, and the adaptation of large scale line-work are the core tasks.
从这些表面可以得出轮廓和大量的候选点测深。 从可用的源中选择功能，然后进行子流程(例如细化，关键的声音指定以及与现有源的冲突)，无论是通过新版本(替换)还是更新，这些功能都将用于编译结果图表。 从一般的角度来看，从可用资源中选择“适当的”深度向量，以及适应大规模的线路工作是核心任务。
This selection must be clear, consistent, and safe for the end-user of the chart.
对于图表的最终用户，此选择必须清晰，一致且安全。
Generalization in terms of marine charts is often used to define cartographic generalization, the stage of viewing the underlying geospatial data. In this model, the representation of the chart features is transformed via a set of fixed generalization operators into viewable representations, the chart symbols. In 1988 McMaster and Shea defined a conceptual model of generalization grouped into:
在海图，以g eneralization通常用来定义制图综合，查看基础地理空间数据的阶段。 在此模型中，统计图特征的表示通过一组固定的归纳运算符转换为可视表示(统计图符号)。 1988年，McMaster和Shea定义了概化的概念模型，归纳为 ：
1. Why — the basis for understanding why generalization takes place
1. 为什么-理解为什么进行概括的基础
2. When — establishing when particular features require generalization
2.何时—确定何时需要概括特定功能
3. How — the exact process of generalization, such as simplification, aggregation, displacement, and elimination.
3.如何—概括的确切过程，例如简化，聚合，置换和消除。
The process of generalization of marine geospatial data making up charts and ENC data can be viewed in this light and used to then define where in the compilation process Artificial Intelligence and Machine Learning (AI/ML) can make a positive contribution, for example, should AI/ML define “when” a sounding, contour or obstruction is generalized or “how” it is generalized in terms of its representation in each chart?
可以从这个角度查看组成图表和ENC数据的海洋地理空间数据的一般化过程，然后将其用于定义在编译过程中人工智能和机器学习(AI / ML)可以在哪些方面做出积极贡献，例如， AI / ML定义了“何时”对声音，轮廓或障碍物进行概括，或者“如何”根据每个图表中的表示进行概括？
The primary difficulty of generalizing depths is the subjective nature of what constitutes a “safe” and informative selection of depth information for use by the end-user. According to IHO S-4, a generalization of depth information should result in a blend of an informative, shoal-biased, and context-sensitive selection of depths at a smaller scale. There are two critical tests referenced in IHO S-4 (B-410) which are crucial to any consideration of the quality of a generalization process, the triangle test and edge test which define a shoal-biased triangulation of soundings (and, potentially, depth areas and other features with bathymetric content) which the mariner can use to interpolate depths in relation to their individual vessel draught and safety margins.
归纳深度的主要困难是构成“安全”和内容丰富的深度信息供最终用户使用的主观性。 根据IHO S-4 ，深度信息的一般化应导致在更小范围内混合深度信息，浅滩偏向和上下文相关的深度选择。 在IHO S-4(B-410)中引用了两个关键测试，这些测试对于综合处理质量的任何考虑都至关重要，即三角形测试和边缘测试，它们定义了测深的三角偏测(并且可能深度区域和具有测深内容的其他特征)，海员可以使用这些深度来插补相对于其个人船只吃水深度和安全裕度的深度。
The IHO mechanism described in IHO S-4 assures the mariner safe passage between soundings by eliminating shoal source soundings between, or on the edge of geodesics joining adjacent soundings (recent work by the University of New Hampshire expanded on these tests’ implementation heavily by adding linear contours and some contextual features to the test’s domain — this does not change the test in principle but enhances its applicability). The following image shows an example triangulation of multiple usage band data together with depth contours and illustrates some of the generalization techniques and the constraints placed upon them by the triangle/edge tests. In it, coastal and approach soundings are in blue and red and the triangulation shows the consistent validation of the triangle test in the soundings selected for inclusion in the generalized coastal chart. The green depth contours are generalized from the approach chart and show how the simplified geometry harmonizes with the selected soundings and simplify the detail of the approach ENC data.
IHO S-4中描述的IHO机制通过消除与相邻测深点相连的大地测量学之间或之间的浅滩源测深，确保了测深之间的航海安全通道( 新罕布什尔大学的最新工作通过增加以下内容极大地扩展了这些测试的实现)线性轮廓和测试领域的某些上下文特征-原则上不会改变测试，但会增强其适用性 。 下图显示了多个使用带数据与深度轮廓的三角剖分示例，并说明了一些泛化技术以及三角形/边缘测试对它们施加的约束。 在该图中，沿海和进近测深为蓝色和红色，并且三角剖分显示了在选定要包含在广义沿海图表中的测深中对三角检验的一致验证。 绿色进深轮廓线是从进近图概括而来的，显示出简化的几何形状如何与选定的测深相协调，并简化进近ENC数据的细节。
Author image 作者形象The other points in S-4 (and which may be enhanced/reflected in individual member state guidance) are the requirement to take into account the density of soundings in respect of seabed morphology and proximity to other contextual features such as hazards and shorelines, all within the constraints of feature/vertex density to reduce the clutter of the resulting chart.
S-4中的其他要点(可能会在各个成员国的指南中得到增强/体现)是要求考虑到海床形态以及与其他背景特征(如危险和海岸线)的接近程度，测深的密度。在要素/顶点密度的约束范围内，以减少结果图表的混乱情况。
The interconnected nature of bathymetric elements can be seen in the following diagram which highlights just two of the key features making up the complex interrelationships in a navigational chart:
下图显示了测深元素的相互联系的性质，该图中仅着重了构成导航图中复杂相互关系的两个关键特征：
Author Image 作者形象Generalization of depth is not dealt with exhaustively in IHO S-4, nor in other cartographic guidance within the existing standards base which leaves member state producers to develop their own detailed guidance and styles. Indeed many ENC datasets are digitized from historical paper charts and therefore retain the generalization styles and features in place for many years.
深度G的eneralization不处理详尽IHO S-4 ，也没有在现有标准的基础叶成员国生产商制定自己的详细指导和风格，其内的其他制图指导。 实际上，许多ENC数据集都是从历史纸质图表中数字化的，因此保留了多年的概括样式和功能。
Although, as previously stated, a large body of knowledge exists inland mapping in respect of generalization, little has been written specifically on the topic of marine cartographic generalization, nor of the bathymetric element of that process. Measurements like Topfer’s ratio equating feature density at different resolutions are useful and processes such as the Douglas-Peucker algorithm for smoothing linear features require extensive adaptation for use within the safety-critical processes in marine charting.
尽管如前所述，在内陆制图方面存在大量的知识，但是关于海洋制图泛化或该过程的测深要素的论述很少。 诸如Topfer比率等于不同分辨率下的特征密度之类的测量非常有用，并且诸如Douglas-Peucker算法(用于平滑线性特征)之类的过程需要广泛的适应性，才能在海洋制图的安全关键过程中使用。
So, the difficulties of automating generalization (and specifically bathymetric generalization) have traditionally been :
因此，自动进行概括(尤其是测深综合)的困难传统上是：
1. Some aspects of generalization can be highly subjective and resist rigid rules-based formulation. Within marine cartography decluttering of charts is of prime importance and aesthetic judgments have played a strong role in the creation of high-quality products for many years.
1.概括的某些方面可能是高度主观的，并且会抵制基于规则的严格表述。 在海洋制图学中，图表的杂乱化是至关重要的，多年来，美学判断在创建高质量产品中发挥了重要作用。
2. Marine cartography places strict safety-related rulesets around generalization due to the extraordinary amount of legal liability inherent in the product. Some examples of this are the generalization of obstructions and hazards relevant to IMO functions in the ECDIS and generalization of coastline/depth areas to ensure safety margins are maintained and reproduced. This requirement impacts on the ability to reuse many terrestrial mapping generalization techniques. Bathymetric data, shoals, obstructions, and contours are features on which navigational decisions are made and where mistakes and omissions can result in profound safety issues, carrying large liabilities for producing nations.
2.由于产品固有的大量法律责任，航海制图围绕泛化制定了严格的安全相关规则集。 例如，在ECDIS中推广与IMO功能有关的障碍物和危害，并推广海岸线/深度地区以确保维持和再现安全裕度。 此要求影响重用许多地面映射概括技术的能力。 水深数据，浅滩，障碍物和等高线是进行航行决策的地方，错误和遗漏会导致严重的安全问题，对生产国承担重大责任。
3. How new / changed information is harmonized with existing information is a characteristic specific to marine cartography because of the large amount of uncertainty involved and the cost of acquisition of raw data.
3.由于涉及的大量不确定性和获取原始数据的成本，如何将新的/更改的信息与现有信息相协调是海洋制图的一个特定特征。
4. There is an implicit spatial and semantic interaction between features in a chart. So, for instance, lateral buoyage close to shore should not be absorbed by the seaward generalization of coastline (via its underlying depth areas and land areas). Bathymetric generalization must take into account seabed morphology when determining the appropriate density of included soundings, it must also take into account proximity to the coastline, significant hazards, and navigational context (e.g. when determining critical soundings in confined approaches). At all times the topology and relationships between features in the datasets need to be maintained.
4.图表中的要素之间存在隐式的空间和语义交互。 因此，例如，靠近海岸的横向浮标不应被海岸线向海的泛滥所吸收(通过其下伏的深度区域和陆地区域)。 在确定包括的测深的适当密度时，测深综合必须考虑海床的形态，还必须考虑到海岸线的接近性，重大危害和航行环境(例如，在密闭进近中确定关键测深时)。 任何时候都需要维护数据集中的拓扑和要素之间的关系。
5. The selection of appropriate bathymetric data from the survey for use in multiple scales must be consistent with neighboring charts and meet the concrete tests defined in procedures (and IHO standards).
5.从勘测中选择合适的测深数据以用于多种尺度，必须与附近的海图一致，并符合程序( 和IHO标准 )中定义的具体测试。
ENC, the primary cartographic product under SOLAS refines the concept of charts somewhat. ENC is a database of geospatial features used to render a chart image on an ECDIS dependent on a number of user-defined parameters according to fixed international standards for content and portrayal. ENC also has a very rigid topological structure and tight validation rules which only permit certain geospatial relationships and feature/attribute combinations. Real-world features are encoded from a number of sources and expressed via the S-57 object/attribute catalogs using a style derived mainly from the IHO Use of the Object Catalogue. This language of features and attributes is symbolized by an ECDIS for display but also for alarm and indication behavior, the safety-critical functions of the navigation system. Bathymetric data is the most important feature class within the chart with many of the IMO mandated safety-critical functions determined from features with bathymetric content.
ENC是SOLAS下的主要制图产品，在某种程度上完善了图表的概念。 ENC是一个地理空间数据库，用于根据固定的内容和刻画国际标准，根据许多用户定义的参数在ECDIS上绘制图表图像。 ENC还具有非常严格的拓扑结构和严格的验证规则，仅允许某些地理空间关系和要素/属性组合。 现实世界的功能从许多来源进行编码，并通过S-57对象/属性目录使用主要源自IHO使用对象目录的样式来表示。 功能和属性的这种语言由ECDIS表示，用于显示，但也用于警报和指示行为，即导航系统的安全关键功能。 测深数据是图表中最重要的要素类，其中许多IMO强制性的安全关键功能是根据具有测深内容的要素确定的。
From an ENC perspective, bathymetric data is held within
从ENC角度来看，测深数据保存在
· Sounding Arrays (SOUNDG)
·探测阵列( SOUNDG )
· Depth Areas (DEPARE) (+Dredged Areas DRGARE) with (DRVAL1/DRVAL2) attributes. Associated depth contours (DEPCNT) are linked with DEPARE features. Additionally routing measures such as deepwater routes and fairways have depth attribution within them which should be considered.
·具有( DRVAL1 / DRVAL2 )属性的深度区域( DEPARE )(+挖泥区域DRGARE)。 关联的深度轮廓(DEPCNT)与DEPARE要素链接。 另外，深水路线和航道等路线测量方法在其中应具有深度归因。
· VALSOU attributes on hazards, subsurface obstructions, and wrecks.
·有关危险，地下障碍物和沉船的VALSOU属性。
All these features make up the bathymetric picture of the ENC and are relevant to generalization processes. Bathymetric cartographic generalization, therefore, in ENC terms needs to preserve the safety-critical nature of certain features as well as delivering a de-cluttered and intuitive presentation of the bathymetric features at all scales. For presentation at smaller scales, therefore, a harmonized approach across all relevant feature types is called for.
所有这些功能构成了ENC的测深图，并且与泛化过程有关。 因此，以ENC术语表示，测深制图一般化需要保留某些要素的安全性至关紧要的性质，并以各种尺度提供整洁而直观的测深要素表示。 因此，为了以较小的比例显示，需要在所有相关要素类型上采用统一的方法。
There is much work on automated cartographic generalization already established within the terrestrial mapping domain, mainly concerned with the definition of symbology generalization operators, rule-based transformations of feature representation, and their integration together. Symbology generalization for ENC however is restricted to the S-52 visual library (so, for instance, line weights cannot be adjusted, nor colors).
Ť这里是自动化制图综合多工作的地面测绘领域内已经建立，主要关心的符号泛化运营商，特征表示的基于规则的转变的定义，以及它们整合到一起。 但是，ENC的符号系统化仅限于S-52视觉库(因此，例如，线宽不能调整，颜色也不能调整)。
This places a tight vocabulary around what generalization processes are definable and how they should be implemented and suggests an approach based on the vector content of the features and attributes rather than from their appearance on screen
这围绕可定义的概化过程以及应如何实施这些概论，并提出了一种基于特征和属性的向量内容而不是根据其在屏幕上的外观的方法。
The proposed system platform is shown in the following diagram:
下图显示了建议的系统平台：
In the proposed system the following steps take place:
在建议的系统中，执行以下步骤：
1. The input training ENC data is split into its component features. Other input data that may be relevant, such as source bathymetric surfaces and soundings and chart metadata will be digitized into the schema within the system. At this point, an automated process determines the extent and content of the existing generalization within the input cells. This is used to form the generalization labels according to the model configuration.
1.输入的培训ENC数据分为其组成特征。 其他可能相关的输入数据，例如源测深曲面和测深以及图表元数据，将被数字化到系统内的模式中。 此时，自动化过程将确定输入像元中现有概括的程度和内容。 这用于根据模型配置形成概括标签。
2. Features that are linked (for instance coastline (COALNE) which is coincident with Land Areas and 0m Depth Areas) are represented as single instances with combined attribution to maintain their validity (e.g. to avoid a depth area being generalized and not matching the appropriate depth contour). From a generalization perspective, it is the underlying skin of the earth features and points soundings/bathymetric attribution which require generalization, not the coastline features.
2.链接的要素(例如，与陆地区域和0m深度区域重合的海岸线(COALNE))被表示为具有合并属性的单个实例，以保持其有效性(例如，避免深度区域被概括且与适当的区域不匹配)深度轮廓)。 从一般化的角度来看，需要综合性的是地球要素和点测深/测深属性的潜在表皮，而不是海岸线特征。
3. A model (selected from a number of candidates) is trained, tuned, and used to predict generalized forms of the input features. These are formed from the predictions by using values generated by the models (i.e. selections of soundings from source or inclusion/exclusion instructions based on chart context (e.g. controlling depths)) and parameters that can drive line smoothing algorithms.
3.对模型(从许多候选项中选择)进行训练，调整并用于预测输入特征的广义形式。 这些是通过使用模型生成的值(即，基于图表上下文(例如，控制深度)从源或包含/排除指令中选择测深)和可以驱动线平滑算法的参数从预测中形成的。
Author Image 作者形象4. A subset of the component features is used to test the model predictions.
4.组件特征的子集用于测试模型预测。
5. The features are re-assembled into a candidate generalized ENC.
5.将特征重新组合成候选的通用ENC。
6. This ENC can then be evaluated against
6.然后可以根据该ENC进行评估
a. Validation rules, IHO S-58, and IHO S-57 UOC.
一个。 验证规则 IHO S-58 和IHO S-57 UOC。
b. IHO S-4 triangle/edge test, national policy tests.
b。 IHO S-4 三角形/边缘测试，国家政策测试。
c. Feature density and compilation scale assessments
C。 特征密度和编译比例评估
d. Safety criteria, safety-critical features as defined under IMO SOLAS
d。 安全标准，IMO SOLAS定义的关键安全功能
e. The cartographic judgment of the effectiveness of the generalization
e。 制图判断的有效性
7. Feedback from the outputs is used to tune the model parameters and modify the feature data designs and labels.
7.来自输出的反馈用于调整模型参数并修改特征数据设计和标签。
As noted in this section, a loss function defining a measure of generalization based on the many algorithms specific to bathymetric data and attribution and factors relevant to bathymetric generalization is used by the system to progressively improve the generalization processes used to form the results.
如本节所述，系统会使用损失函数，基于特定于测深数据和归因的许多算法以及与测深泛化相关的因素来定义泛化度量，以逐步改善用于形成结果的泛化过程。
It is crucial to ensure as large a training dataset as possible is available to the system — in bathymetry terms, this should also contain the processed source data from which soundings/contours are derived and which form the decision space for the majority of the soundings/contours.
确保系统可以使用尽可能大的训练数据集至关重要-以测深法而言，它还应包含处理后的源数据，从中得出测深/等高线，并形成大多数测深/轮廓。
The success of such a system will be heavily dependent on the availability of a critical mass of representative training data at all scales and the generalization processes defining the input ENC cells.
这种系统的成功将在很大程度上取决于在所有规模上是否有关键数量的代表性训练数据，以及定义输入ENC单元的概括过程。
Posted by Ocean News 由海洋新闻发布This pipeline technology has the following benefits:
这种管道技术具有以下优点：
1. It is platform neutral and uses only open source components, Java, Python, PostgreSQL/PostGIS and can be adapted to other spatial database solutions.
1.它是平台无关的，仅使用Java，Python，PostgreSQL / PostGIS等开源组件，并且可以适应其他空间数据库解决方案。
2. It allows any AI/ML model to be interfaced with its open schema without any proprietary restrictions whatsoever. This allows for maximum flexibility in choice, tuning, and configuration of the machine learning model, crucial given the number and variation available to the project.
2.它允许任何AI / ML模型与其开放式架构接口，而没有任何专有限制。 这就给机器学习模型的选择，调整和配置提供了最大的灵活性，这在给定项目可用数量和变化的情况下至关重要。
3. The open architecture allows for multiple algorithms to be engineered to generate line features (and associated polygons). This means that depth contour/depth area generalization can be accomplished by using machine learning to learn “parameters” such as offsets from existing contours, inclusion/exclusion of shoals, and identification of critical depths, and then the actual algorithms generating the features can be deterministic rather than defined by the machine learning model.
3.开放式体系结构允许设计多种算法以生成线要素(和关联的多边形)。 这意味着深度轮廓/深度区域的概括可以通过使用机器学习来学习“参数”，例如从现有轮廓偏移，浅滩的包含/排除以及关键深度的识别，然后生成特征的实际算法可以完成。确定性的，而不是由机器学习模型定义的。
4. The system would not be limited to feature generalization only. Assessment of for example SCAMIN application (e.g. selection of SCAMIN values on safety-critical soundings) and safety classification of changes to ENCs would be alternative use cases for such an AI/ML adapted system.
4.该系统将不仅限于特征概括。 对于这样的AI / ML适应系统，评估SCAMIN应用(例如，选择对安全至关重要的SCAMIN值)以及对ENC进行更改的安全分类将是替代用例。
5. These components also allow “hybrid” ENCs to be created where some elements are generalized whereas others are untouched. This has the advantage of allowing the project to progress iteratively with more complex generalization included when simple cases are initially proven.
5.这些组件还允许在某些元素被概括而其他元素未被修改的情况下创建“混合” ENC。 这样做的好处是，当最初证明简单的情况时，项目就可以以更复杂的概括来迭代进行。
6. The Nautilus process preserves the relationships between the features and their topology so that a standards conformant ENC can be built for full validation/inspection after the prediction processes have run. The system allows for training validation to take place for the classification process to complete.
6. Nautilus流程保留了要素及其拓扑之间的关系，因此可以在运行预测流程后构建符合标准的ENC，以进行全面的验证/检查。 该系统允许进行培训验证，以完成分类过程。
7. The maximum flexibility of the input data and its labels is achievable.
7.可以实现输入数据及其标签的最大灵活性。
翻译自: https://towardsdatascience.com/cartographic-generalization-with-a-i-and-machine-learning-db65b52f45c4
机器学习 深度学习 ai
