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San Diego, CA 92101

619-533-4300sdfd@sandiego.gov

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About SDFD | City of San Diego Official Website

Enable

Main navigation

Home

Leisure

Resident Resources

Doing Business

Library

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City Hall

City of San Diego Official Website

Partly Cloudy 65°

Accessibility Tools

Type

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Home

Leisure

With its great weather, miles of sandy beaches, and major attractions, San Diego is known worldwide as one of the best tourist destinations and a great place for residents to relax year round.

Parks

Regional Parks

Community Parks

Recreation Centers

Book an Event

Outdoors

Beaches & Bays

Golf

More Leisure

Balboa Park

Torrey Pines Golf Course

Special Events & Filming

Arts & Culture

Resident Resources

From neighborhoods and parks to streets and parking, find what you need in your community and report your concerns.

Neighborhoods

Community Plans

Recreational Activities

Pools

Street Maintenance

Potholes

Street Sweeping

Streetlights

More Resident Resources

View/Pay Water Bill

View Trash Collection Schedule

Parking Citations

City Strategic Plan

Doing Business

Incentives and programs are available to help launch, grow and expand your business, and provide support for homeowners and contractors to get work done.

Plan

Bid on a Contract

Explore Incentives

Development Codes & Regulations

Fix

Set Up Utilities

Renovations & Remodels

Build

Get Permits

Track Your Project

Learn about Zoning

Business Tax Certificate

Ten Key Steps to Starting Your Business

Go Green

Library

The San Diego Public Library is a popular destination that connects our diverse community to free educational and cultural resources that will enrich their lives.

Programs & Events

One Book, One San Diego

Event Calendar

More Programs & Events

Services

Fines & Fees

Renewals & Holds

More Services

Kids & Teens

Homework Help Center

Parents & Teachers

More Kids & Teens

eCollection

eBooks

Digital Magazines

Library Website

Browse the Catalog

Apply for a Library Card

Volunteer

Employment Opportunities

Public Safety

Police

File a Police Report

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Join SDPD

Police Website

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Council Meetings

Council Committees

FY24 Adopted Budget

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San Diego Fire-Rescue Department Home

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San Diego Fire-Rescue Department Home

About SDFD

News Center

Safety Education

Services & Programs

Community Risk Reduction

Careers

Contact SDFD

About SDFD

The City of San Diego's Fire-Rescue Department encompasses all fire, emergency medical, lifeguard and emergency management services. This includes 9-1-1 services, fire inspections, permits and community education.

Facts and Figures

343 square miles

Population 1,419,845

17 miles of coastline extending 3 miles offshore 4,600 acres around Mission Bay Park

52 fire stations

9 permanent lifeguard stations (31 seasonal stations during peak period)

949 uniformed Fire personnel

98 Permanent uniformed Lifeguard personnel

246 civilian personnel

Incidents CY 2023

Fire

6,006

3.3%

Rescue

1,488

0.8%

Life-Threatening Emergency Response

109,143

59.5%

Urgent Response

27,512

15.0%

Non-Life-Threatening Response

21,766

11.9%

Hazard

14,105

7.7%

Service

1,071

0.6%

Events

266

0.1%

Other

1,993

1.1%

Total

183,350

100%

Mission Statement

To serve the community of San Diego by providing the highest level of emergency/rescue services, hazard prevention and safety education ensuring the protection of life, property and the environment.

About SDFD

About SDFD Home

Fire Chief Colin Stowell

Apparatus

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Charitable Partnering Opportunities

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Department Organization

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Department Directory

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Council Districts

Councilmember Joe LaCava (District 1)

Councilmember Jennifer Campbell (District 2)

Councilmember Stephen Whitburn (District 3)

Council District 4

Councilmember Marni von Wilpert (District 5)

Councilmember Kent Lee (District 6)

Councilmember Raul Campillo (District 7)

Councilmember Vivian Moreno (District 8)

Council President Sean Elo-Rivera (District 9)

City Officials & Independent Offices

Office of Mayor Todd Gloria

City Attorney Mara W. Elliott

Ethics Commission

Office of the City Auditor

Office of the City Clerk

Office of the City Treasurer

Office of the Independent Budget Analyst

Personnel Department

Government Agencies

County of San Diego

State of California

Federal Government

Disclaimers

Accessibility

Language Translation

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谈谈DeepSDF中AutoDecoder - 知乎首发于人工智障领养中心切换模式写文章登录/注册谈谈DeepSDF中AutoDecoderClyce一个游离的玻尔兹曼脑文章同步发布于简书：日后我的写作重心可能会逐步切换到简书平台，因为它更适合沉下心去读写一些东西。前阵子读了一篇关于三维重建的论文，是Facebook团队联合华盛顿大学发表在CVPR2019上的。这一论文尝试用神经网络去近似SDF函数（将在后面进行简述），进而重建三维模型。在阅读过程中我对其中的AutoDecoder概念产生了一定兴趣，特此将一些随想记录下来。在谈论AutoDecoder之前，我想先用最少的文字说说这个传说中のDeepSDF。用最简单易懂的语言来说，对于一个已知的三维模型，SDF(Signed Distance Function)就是一个三维空间中的函数，输入一个坐标点 p_0 = (x_0, y_0, z_0) ，返回该点到三位模型表面的距离。同时这个距离是有符号的，一般来说，对于模型外面的点，距离为正，而模型内部的点则为负。或者从观察者的角度，如果点在表面面向观察者的一侧，则为正，在背离观察者的一侧，则为负。引用DeepSDF论文官方的兔兔说明一下：各个方法对于SDF函数的存储往往是离散的，比如将三维空间是做一系列的“体素”（类比二维空间的“像素”），每个体素中存储一个SDF值，之后尝试用体素中等于或接近零的部分重建一个三维表面。而DeepSDF论文呢，则尝试将SDF解析为一个连续的函数。虽然文章把这一点作为一个创新提出（包括将模型表面视作SDF回归的决策边界），其实我觉得这个很早应该就有人想到了，因为根本上来讲，SDF本来就应该是一个连续函数才对，只是受制于各种原因，我们无法为每个三维物体写出这个连续函数的完整形式。这也就是DeepSDF的思路，用神经网络这个“万能插值机”去近似这一函数。事实上我之前也想过这个方向，也相信许多人尝试过这个思路。不过就像大多数DeepLearning落地的应用一样，真正需要做的工作在于如何科学地编码输入输出，以及如何Encapsulate你自己的领域知识到神经网络的结构和训练过程当中去。文章一开始提出了一个显而易见的结构，就是直接拿个神经网络，输入坐标，输出SDF值，然后对每一个三维模型单独训练，充分发挥“插值机”的原始作用。不过显然，这种方法在现实中是很难应用的，因为对于每个新模型都要训练一个新神经网络，实在是太低效太不~~Generalizablism~~了，那么自然地，为了发挥深井网络Generalizability的优势，团队自然而然想到了使用一个Latent Vector去表示三维模型的原始形态，于是新的神经网络就变成了，给定一个用来表示三维模型的Latent Vector，附带一个用以查询的坐标点，返回这个坐标点所在位置的SDF值。具体的神经网络结构和LossFunction的设计就不详述了，非常Straight forward，扫一眼论文就明白。众所周知（~~并不~~）获得一个原始输入数据的Latent Vector Representation，大家最喜欢的方法之一就是训练一个AutoEncoder。然而DeepSDF则使用了一个稍有不同的东西，他们叫做AutoDecoder，故名思义，就是一个不训练Encoder的Decoder。这里我想吐个大嘈，他们的论文说了一大堆不要Encoder的理由，比如AutoEncoder大家一般都只用一半而把另一半另一半扔掉什么的，说来说去没说到点上。强烈怀疑他们组提出使用AutoDecoder架构的人和最后执笔写论文的人沟通不畅。我自己读下来的感觉是，AutoDecoder的选用本质上是一个在FeedForward过程的速度，和数据本身潜在的复杂度和变数之间的取舍（如果我的理解有错，欢迎指正）下面简单讲一下这个传说中の AutoDecoder：如图所示，标准AutoEncoder的架构大体上是，一个输入，若干层（称为Encoder）之后有一个关键的瓶颈层（Code），瓶颈层后面若干层（称为Decoder）之后有一个和输入层长得一样的输出层。训练的时候努力让输出等于（甚至优于——比如修复类任务）输入，最后训练出来的瓶颈层，就是对输入数据的一种更紧凑的压缩，也叫做关于原始数据的Latent Vector。Latent Vector往往包含了原始数据最关键的那些信息，更加便于提取特征，等等等等。而DeepSDF则提出，我们可以不要Encoder部分，而直接取用Decoder部分。这就很TM诡异了，如果我只有一个Decoder，我怎么知道哪个Latent Vector对应原始输入呢？官方给出的示意图长这样： Hmm...不是很好懂，没错，不仅这图不太好懂，论文里的描述也乱七八糟，不过且慢，容我再画一张图：这张图里，我们加入了一个额外的输入 X \equiv 1 ，称为第0层，而 Code 层则是一个纯 Linear 层，那么第 0 层对 Code 层的权重 W_0 即是我们要的 Latent Vector。另外，Decoder 不必是解码成原始数据的样子，而是只要解码成目标函数的输出（比如SDF值）就好了。那么对于DeepSDF来说，其结构事实上可以理解成： 1. 第 0 层是一个恒为1的输入， 2. Code 层和要查询的 (x,y,z) 一起输入到后面的深度神经网络 3. 最后输出SDF函数而这个训练过程则是：对每个模型 i ，初始化一个独立的Latent Vector v_i （嗯，原论文用 z 表示Latent Vector，不过为了避免和空间坐标点 (x,y,z) 产生迷惑，我在这里用 v 代替了）然后对于每个 ((x,y,z),\text{sdf})^i 样本，选择该Vector作为到Code层的权重，即 W^i_{(0\to\text{code})} = z_i^\top ，再对整个神经网络（包含 W^i_{(0\to\text{code})} ）进行训练。这样子训练下来，每个训练样本都渐渐就训练出了自己的Latent Vector，而Decoder则学习到了对每个Latent Vector，如何匹配其要查询的 (x,y,z) 对应的sdf值。可是既然这样，每个训练数据都训练出了自己独立的Latent Vector，那么面对新的数据（比如测试数据集）该怎么办？论文里轻描淡写了一个 \text{argmin}_v ，可是这个 \text{argmin} 是怎么做的呢？难道还是对每个新数据重新训练一番？！这里不得不吐槽一下，这个项目不仅论文抓不住重点，代码也写得乱七八糟，读得人晕头转向，重建的核心部分居然还存在# TODO: why is this needed这样的注释。我准备过一阵子专门针对机器学习界软件工程基础薄弱，代码凌乱不堪的现象开一篇文章。于是带着迷惑我又去看了一遍这个项目的源码，然后发现还真的（差不多）是这样。。。不过比起直接用 (x,y,z) 作为输入的那个版本，这个版本在面对新数据 t 的时候，锁定了整个Decoder，也就是说保持 (v, (x,y,z)) \to \text{sdf value} 的映射不变，而只训练新的 v_t^\top = W^t_{(0\to\text{code})} ，大大减少了需要训练的参数数量。并且因为Decoder经过之前的训练，已经包含了关于训练数据的先验知识，所以这次Latent Vector训练的收敛将会十分迅速。其实这里面我有一个小小的疑惑，为什么不保留论文最开始的结构（只输入 (x,y,z) ），但是对于每个不同的模型，独立训练输入层对第一个隐藏层的权重，这样输入层对第一个隐藏层的变换可以视作一个“Latent Function”（对应“Latent Vector”的概念），将 (x,y,z) 的值变换为该模型对应的，隐空间下该点的特征。这样一来，神经网络的参数数量能够减少，并且也能够更好地表达Latent Coding和空间坐标之间的耦合关系。这一方面的比较和分析，如果有时间，我也有可能会去做一做。所以，答案是，AutoDecoder的结构不能像AutoEncoder一样面对新数据时只需要一个Forward过程，而是需要根据后面的Decoder中隐含的先验知识，进行少量的训练，以找到一个Latent Code出来。那么问题来了，为什么要使用一个无法进行实时推断而是每次都要训练的AutoDecoder来代替原有的AutoEncoder呢？一个不那么具备说服力（但是论文里似乎确实提出了）的理由是，因为Decoder不必还原原始数据，而可以是近似目标函数，所以训练出来的Latent Vector，对于我们目标函数所需要的信息会更加友好。但是仔细想一想就会发现，如果我们直接使用原始数据进行输入，构建一个神经网络，然后在某一隐藏层拼接上 (x,y,z) 查询。那么训练之后，我们就可以得到一个在预测时只需要Forward过程而无需再训练的神经网络，而被拼接了 (x,y,z) 的那一层也就可以视作Latent Vector。所以……用AutoDecoder的深层原因到底是什么？于是我去费劲巴拉地又读了一遍他们的代码，着重看了看Reconstruction部分，想办法搞明白这个神经网络到底在做什么之后，谜题便渐渐解开了。首先，这个模型在重建的时候到底在干什么？这个问题在我读论文和代码的时候一度困扰了我很久，尤其是在我看到这个预期输出是SDF Value的神经网络在 Test - Evaluation 阶段，依然在测试数据集中包含SDF Value进行训练。进一步捋顺之后我反应过来他们在神经网络中的那一步Evaluation，实际上是在衡量这个神经网络能构造出一个多好的Latent Vector来近似测试数据集中的SDF Value，而不是尝试计算Reconstruct Error。而在真正的表面重建过程中，这个收敛出来的Latent Vector将和任意输入坐标一起得出一个SDF Value，进而实现任意精度的重建。那么问题就逐渐明朗了，DeepSDF在做的事情，其实是对于有限精度采样的SDF数据，在包含先验知识的Decoder的帮助下，回归出一个连续的SDF函数，从而实现将有穷精度的数据转换成任意精度的查询器，以便进行精细重建。返回来看这个AutoDecoder在测试数据上的训练过程（即寻找Latent Vector的过程），我们发现，可以将一个模型的所有采样点作为一个完整的epoch，而不是把单个模型的全部采样数据一股脑输入进去。这么做的好处是什么呢？如果是AutoEncoder，为了得到代表一整个模型的Latent Vector，我们将不得不想办法统一模型原始数据的分辨率（比如每三个神经元代表一个采样点，那么一个模型最多只能有input_size / 3个采样点）。而对于AutoDecoder，一个模型对应的采样数量只影响其epoch的大小，而不会影响单个输入本身的尺寸，所以AutoDecoder的架构支持对每个输入模型有任意数量的采样点。另一个非常重要的是，对于模型的采样点，一个显著的特征就是它是Orderless的，即你交换任意两个采样点的顺序，也不应当影响最终输出的结果。传统的神经网络结构并不是很善于解码orderless vector的输入，但是如果我们不将整个模型的所有采样点编码为一个巨大的input vector，而是将每个采样点作为一个input vector，整个模型的所有采样点作为一个epoch去训练，那么我们就可以应对这种orderless的特征了。综上，我们可以得到AutoDecoder结构与AutoEncoder以及论文最开始提出的 (x,y,z) \to \hat{\text{SDF}} 结构（下面简称xyz结构）进行比较：AutoDecoder vs xyz结构：对于新的模型，可以比xyz更快速地收敛成一个可靠的查询器经过训练的Decoder包含了来自训练数据集的先验知识，使得对于SDF的回归并不是单纯地“插值”操作，而是考虑了模型本身附带的意义而进行的合理补全。AutoDecoder vs AutoEncoderAutoDecoder能够处理任何尺寸的采样数据，而不必像AutoEncoder那样将一个模型的全部采样点包含在一个定长的input vector中AutoDecoder将单独的Input Vector转换成Epoch的操作，使得采样的顺序能够被很好地解耦，适合处理orderless的采样数据相比AutoEncoder来说，AutoDecoder面对每一个新数据时都需要一个训练过程来找到合适的Latent Vector，无法在需要相对实时的领域应用到这里，AutoDecoder的具体合适的应用场景，可谓呼之即出了。不过最后我想说一个自己的疑惑，不知道大家怎么看待：将单个模型的所有采样数据作为一个Epoch而非一整个Input Vector去获取Latent Vector的这一操作，是否因为神经网络的Independence Assumption而丢失了邻接节点的互相影响呢？鄙人不才，没想到足够的支持或者否定，还希望大家讨论点拨编辑于 2020-01-15 16:48计算机视觉机器学习赞同 17022 条评论分享喜欢收藏申请转载文章被以下专栏收录人工智障领养中心人工智障的养成

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San Diego police, SDFD lifeguards share details of 911 calls, rescues from January floods – NBC 7 San Diego

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San Diego

San Diego police, SDFD lifeguards share details of 911 calls, rescues from January floods

The majority of calls were received from the Southcrest and Encanto neighborhoods

By Dana Williams •

Published February 15, 2024 •

Updated on February 15, 2024 at 8:52 pm

NBC Universal, Inc.

As the community continues to rebuild after devastating flooding in January that destroyed homes in neighborhoods like Southcrest and Encanto, the San Diego Fire-Rescue Department (SDFD) and the San Diego Police Department (SDPD) are sharing more details about the emergency calls they received on Jan. 22.

According to a report presented to San Diego City Council's Public Safety Committee, there were 985 911 calls processed by the dispatch center that day, a 53% increase over average, and 752 SDFD “first responder incidents” within the city of San Diego, which is a 73% increase over average.

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“We’ve practiced for some of this, right, for when the system gets inundated,” Chief James Gartland, who oversees SDFD lifeguards, told NBC 7.

Get San Diego local news, weather forecasts, sports and lifestyle stories to your inbox. Sign up for NBC San Diego newsletters.

Gartland said that day started like any other rainy day. The forecast discussed with the National Weather Service predicted an inch to an inch and a half of rain. The department’s swift water rescue teams were ready and staged at known flood zones, then 911 calls started to pour in.

“We just had, you know, so many calls and there were major, major trouble spots in some of the neighborhoods,” Gartland said. “We were evacuating, you know, 80 people from one street.”

The majority of calls were received from the Southcrest and Encanto neighborhoods. According to Gartland, lifeguards saved approximately 140 people and 30 animals, but it was not easy.

“You have cars floating down, right, you have people trying to cross areas, so all of those hazards are out there,” he said. "That’s why we train as hard as we do.”

Gartland told NBC 7 that lifeguards go to Oregon to train, in part, because they need an environment with rushing water that San Diego doesn’t always have.

“People don’t know, you can’t get the kind of river flow here in Southern California in the time we need to train so lifeguards go away," Gartland said.

Obviously, he said, they could not have prepared for a storm like the one we experienced, but the training they did have helped fill in the gaps. It helped that they recently received new gear from a grant, too.

“We had people out there that were well-trained. They have the right equipment and they executed the mission that we needed them to execute,” he said.

On the other hand, San Diego police officers who were in the field that day were not as prepared, but it didn't stop them from making rescues.

“We don’t carry around things to take people out of houses, evacuate them in water conditions and flooding conditions,” Captain Julie Epperson, with SDPD’s southeastern division, told NBC 7. “My officers were very innovative in that they grabbed paddleboards, they grabbed canoes and they were literally using them just to get people out of the houses on that day because that was the best we had.”

According to Epperson, officers saved approximately 60 people and five animals, all with the limited training and resources they had.

“I think our officers, the ones I’ve talked to and myself as well, we’ve never seen a day like this as far as rain,” she said. “I mean, that will go down in infamy probably as the worst rain that we’ve seen during our tenure here at the police department.”

Epperson has been with the department for close to 30 years. She only recently came to the southeastern division, but said they have been doing what they can to help people recover, including accepting and distributing donations from their division headquarters.

“I will remember it as a tragic day for our community, it was and it still continues to be a tragedy for [residents],” she said. “But, it was a proud moment for us as our officers, and for all first responders, that we did the best we could with having very little time to prepare for it.”

Both Gartland and Epperson agree, there were invaluable lessons learned that will be referenced for generations of San Diego first responders to come.

“I think we now know exactly where the spots are, where the flooding is, the officers know. We’ll learn from that experience,” Epperson said. “Moving forward, our response will improve every time. I hope it never happens again, but if it does, I feel like it was a learning experience for all of us.”

To watch the full presentation presented to the Public Safety Committee by both SDFD and SDPD on their emergency flood response, click here to access the Feb. 14 meeting.

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San Diego’s Public Safety Committee approves SDPD, SDFD use of drone technology – NBC 7 San Diego

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San Diego

San Diego's Public Safety Committee approves SDPD, SDFD use of drone technology

Next, it will need to be approved by the full San Diego City Council

By Dana Williams •

Published February 14, 2024 •

Updated on February 14, 2024 at 8:09 pm

NBC Universal, Inc.

Unmanned aircraft systems, also referred to as UAS systems or drones, have been used by city of San Diego first responders for years.

“In 2018, we had our first mission, and for the next few years, we kind of had a slow rollout while being very careful on the types of missions we would respond to,” Sgt. Nicholas Link with the San Diego Police Department told NBC 7. “It was very much a learning process.”

Link is a certified Federal Aviation Administration drone pilot and the person who developed SDPD’s UAS program. The San Diego Fire-Rescue Department also started their program in 2018. Now, both agencies need to go through the process of being approved under the city’s Transparent and Responsible Use of Surveillance Technology Ordinance (TRUST).

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The ordinance was passed in 2022, then amended earlier this year. It requires that technologies under a broad "surveillance" umbrella used by city employees are revealed to the public and can be evaluated with a privacy advisory board. That includes approximately 300 technologies.

“It’s always been a balance from the beginning,” Link said, in regard to privacy concerns that come with the use of drones. “We have highlighted in our policies and procedures and our training programs a little bit of extra emphasis on understanding what the Fourth Amendment is.”

Link added it is a priority of the department’s to ensure “we always are protecting and respecting people’s privacy while still accomplishing our law enforcement mission.”

As part of the approval process under TRUST, SDPD and SDFD held joint hearings in each of the nine city council districts in early November. It was a requirement but was later amended by the council because of the resources it takes to host them and the lack of attendance from the public. According to Link, only a few people attended.

Then, in mid-November, the agencies presented their programs to the Privacy Advisory Board (PAB). The PAB made several recommendations to both agencies but ultimately suggested that the council approve the use of the drone technology.

Then, the items moved on to the city council's Public Safety Committee. On Wednesday, SDFD and SDPD, including Link, presented to the committee.

“A lot of the recommendations were just simply a desire to change some of the verbiage in the technology use policy,” Link said, referring to the feedback from PAB. “It was actually discovered that the majority of them, the recommendations, were already existing policies and procedures that are documented and we currently use through our department.”

Link said that in the process, so far, no major changes have been made to their drone program. In 2023, they completed approximately 60 missions. He also mentioned they have only been requested twice in six years for special circumstances — both times by city departments. Most recently, one of them was related to the flooding that happened on Jan. 22.

The representative for SDFD who presented also mentioned that many of the flights their drones take are not recorded. They are often used as real-time data for checking hot spots while fighting a fire, for example, and they decided that is not something that needs to be saved. Along the same lines, Link said not all of their flights are recorded, and if they are they are likely considered evidence of a crime, but the public is able to request to see a recording.

The Wednesday meeting ended with unanimous approval of the use of UAS technology pursuant to the TRUST ordinance for both SDFD and SDPD. Next, it will need to be approved by the full city council.

Advocates pushing for more transparency on how those tools are used scored a major win Tuesday.

This article tagged under:

San DiegoSan Diego Police DepartmentSan Diego City CouncilSan Diego Fire-Rescue Department

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